2024
Obidi, Joyce; Sridhar, Gayathri; Dores, Graça M; Whitaker, Barbee; Villa, Carlos H; Storch, Emily; Chada, Kinnera; Schilling, Lisa M; Natarajan, Karthik; Biondich, Paul; Soares, Andrey; Spotnitz, Matthew; Falconer, Thomas; Purkayastha, Saptarshi; Draper, Nicole L; Wong, Hui-Lee; Stagg, Matthew; Reich, Christian; Anderson, Steven; Shoaibi, Azadeh
Patterns of red blood cell utilization: Harnessing electronic health records data from the Information Standard for Blood and Transplant (ISBT) 128 system within the Biologics Effectiveness and Safety (BEST) initiative Journal Article
In: Transfusion, 2024.
@article{obidi2024patterns,
title = {Patterns of red blood cell utilization: Harnessing electronic health records data from the Information Standard for Blood and Transplant (ISBT) 128 system within the Biologics Effectiveness and Safety (BEST) initiative},
author = {Joyce Obidi and Gayathri Sridhar and Gra\c{c}a M Dores and Barbee Whitaker and Carlos H Villa and Emily Storch and Kinnera Chada and Lisa M Schilling and Karthik Natarajan and Paul Biondich and Andrey Soares and Matthew Spotnitz and Thomas Falconer and Saptarshi Purkayastha and Nicole L Draper and Hui-Lee Wong and Matthew Stagg and Christian Reich and Steven Anderson and Azadeh Shoaibi},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Transfusion},
publisher = {John Wiley \& Sons, Inc. Hoboken, USA},
abstract = {Background: Current hemovigilance methods generally rely on survey data or administrative claims data utilizing billing and revenue codes, each of which has limitations. We used electronic health records (EHR) linked to blood bank data to comprehensively characterize red blood cell (RBC) utilization patterns and trends in three healthcare systems participating in the U.S. Food and Drug Administration Center for Biologics Evaluation and Research Biologics Effectiveness and Safety (BEST) initiative.
Methods: We used Information Standard for Blood and Transplant (ISBT) 128 codes linked to EHR from three healthcare systems data sources to identify and quantify RBC-transfused individuals, RBC transfusion episodes, transfused RBC units, and processing methods per year during 2012-2018.
Results: There were 577,822 RBC units transfused among 112,705 patients comprising 345,373 transfusion episodes between 2012 and 2018. Utilization in terms of RBC units and patients increased slightly in one and decreased slightly in the other two healthcare facilities. About 90% of RBC-transfused patients had 1 (~46%) or 2-5 (~42%)transfusion episodes in 2018. Among the small proportion of patients with ≥12 transfusion episodes per year, approximately 60% of episodes included only one RBC unit. All facilities used leukocyte-reduced RBCs during the study period whereas irradiated RBC utilization patterns differed across facilities.
Discussion: ISBT 128 codes and EHRs were used to observe patterns of RBC transfusion and modification methods at the unit level and patient level in three healthcare systems participating in the BEST initiative. This study shows that the ISBT 128 coding system in an EHR environment provides a feasible source for hemovigilance activities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methods: We used Information Standard for Blood and Transplant (ISBT) 128 codes linked to EHR from three healthcare systems data sources to identify and quantify RBC-transfused individuals, RBC transfusion episodes, transfused RBC units, and processing methods per year during 2012-2018.
Results: There were 577,822 RBC units transfused among 112,705 patients comprising 345,373 transfusion episodes between 2012 and 2018. Utilization in terms of RBC units and patients increased slightly in one and decreased slightly in the other two healthcare facilities. About 90% of RBC-transfused patients had 1 (~46%) or 2-5 (~42%)transfusion episodes in 2018. Among the small proportion of patients with ≥12 transfusion episodes per year, approximately 60% of episodes included only one RBC unit. All facilities used leukocyte-reduced RBCs during the study period whereas irradiated RBC utilization patterns differed across facilities.
Discussion: ISBT 128 codes and EHRs were used to observe patterns of RBC transfusion and modification methods at the unit level and patient level in three healthcare systems participating in the BEST initiative. This study shows that the ISBT 128 coding system in an EHR environment provides a feasible source for hemovigilance activities.
Abid, Areeba; Murugan, Avinash; Banerjee, Imon; Purkayastha, Saptarshi; Trivedi, Hari; Gichoya, Judy
AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study Journal Article
In: JMIR Medical Education, vol. 10, pp. e46500, 2024.
@article{abid2024ai,
title = {AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study},
author = {Areeba Abid and Avinash Murugan and Imon Banerjee and Saptarshi Purkayastha and Hari Trivedi and Judy Gichoya},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {JMIR Medical Education},
volume = {10},
pages = {e46500},
publisher = {JMIR Publications Toronto, Canada},
abstract = {Background:
Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty.
Objective:
We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption.
Methods:
This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student’s interest area and career goals. Students’ success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students’ experiences was also collected.
Results:
This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students’ self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential.
Conclusions:
Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty.
Objective:
We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption.
Methods:
This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student’s interest area and career goals. Students’ success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students’ experiences was also collected.
Results:
This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students’ self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential.
Conclusions:
Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care.
Purkayastha, Saptarshi; Patri, Akshita Venkat; Yerrabandi, Vedasree
A Skills Assessment Pathways-Based Program Assessment Approach in Multidisciplinary Graduate Health Informatics Journal Article
In: Studies in Health Technology and Informatics, vol. 310, pp. 1191–1195, 2024.
@article{purkayastha2024skills,
title = {A Skills Assessment Pathways-Based Program Assessment Approach in Multidisciplinary Graduate Health Informatics},
author = {Saptarshi Purkayastha and Akshita Venkat Patri and Vedasree Yerrabandi},
url = {https://ebooks.iospress.nl/doi/10.3233/SHTI231153},
doi = {10.3233/SHTI231153},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Studies in Health Technology and Informatics},
volume = {310},
pages = {1191\textendash1195},
abstract = {Multidisciplinary graduate education programs are hard to assess because of interdependent competencies. Students in these programs come with diverse disciplinary undergraduate degrees, and it is critical to identify knowledge gaps among these diverse learner groups to provide support to fill these gaps. Health Informatics (HI) is a multidisciplinary field in which health, technology, and social science knowledge are foundational to building HI competencies. In 2017, the American Medical Informatics Association identified ten functional domains in which HI competencies are divided. Using pre/post-semester knowledge assessment surveys of graduate students (n=60) between August 2021 to May 2022 in one of the largest graduate HI programs in the United States, we identified courses (n=9) across the curriculum that help build HI-specific competencies. Using statistical analysis, we identified three skills pathways by correlating knowledge gained with course learning objectives and used this to modify the curriculum over four semesters. These skills pathways are connected through one or two courses, where students can choose electives or, in some instances, course modules or assignments that link the skills pathways. Moreover, there is a statistically significant difference in how students gain these skills depending on their prior training, even though they take the same set of courses. Gender and other demographics did not show statistical differences in skills gained. Additionally, we found that research assistantships and internships/practicums provide additional skills not covered in our HI curriculum. Our program assessment methodology and resulting curricular changes might be relevant to HI and other multidisciplinary graduate training programs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kuo, Kuan-Ting; Moukheiber, Dana; Ordonez, Sebastian Cajas; Restrepo, David; Paddo, Atika Rahman; Chen, Tsung-Yu; Moukheiber, Lama; Moukheiber, Mira; Moukheiber, Sulaiman; Purkayastha, Saptarshi; others,
DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries Journal Article
In: arXiv preprint arXiv:2401.11114, 2024.
@article{kuo2024denguenet,
title = {DengueNet: Dengue Prediction using Spatiotemporal Satellite Imagery for Resource-Limited Countries},
author = {Kuan-Ting Kuo and Dana Moukheiber and Sebastian Cajas Ordonez and David Restrepo and Atika Rahman Paddo and Tsung-Yu Chen and Lama Moukheiber and Mira Moukheiber and Sulaiman Moukheiber and Saptarshi Purkayastha and others},
url = {https://openreview.net/pdf?id=eEu49z2L1A},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {arXiv preprint arXiv:2401.11114},
abstract = {Dengue fever presents a substantial challenge in developing countries where sanitation infrastructure is inadequate. The absence of comprehensive healthcare systems exacerbates the severity of dengue infections, potentially leading to life-threatening circumstances. Rapid response to dengue outbreaks is also challenging due to limited information exchange and integration. While timely dengue outbreak forecasts have the potential to prevent such outbreaks, the majority of dengue prediction studies have predominantly relied on data that impose significant burdens on individual countries for collection. In this study, our aim is to improve health equity in resource-constrained countries by exploring the effectiveness of high-resolution satellite imagery as a nontraditional and readily accessible data source. By leveraging the wealth of publicly available and easily obtainable satellite imagery, we present a scalable satellite extraction framework based on Sentinel Hub, a cloud-based computing platform. Furthermore, we introduce DengueNet, an innovative architecture that combines Vision Transformer, Radiomics, and Long Short-term Memory to extract and integrate spatiotemporal features from satellite images. This enables dengue predictions on an epi-week basis. To evaluate the effectiveness of our proposed method, we conducted experiments on five municipalities in Colombia. We utilized a dataset comprising 780 high-resolution Sentinel-2 satellite images for training and evaluation. The performance of DengueNet was assessed using the mean absolute error (MAE) metric. Across the five municipalities, DengueNet achieved an average MAE of 43.92. Our findings strongly support the efficacy of satellite imagery as a valuable resource for dengue prediction, particularly in informing public health policies within countries where manually collected data is scarce and dengue virus prevalence is severe.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Burns, John Lee; Gichoya, Judy Wawira; Kohli, Marc D; Jones, Josette; Purkayastha, Saptarshi
Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study Journal Article
In: PLOS Digital Health, vol. 3, no. 2, pp. e0000297, 2024.
@article{burns2024theory,
title = {Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study},
author = {John Lee Burns and Judy Wawira Gichoya and Marc D Kohli and Josette Jones and Saptarshi Purkayastha},
url = {https://doi.org/10.1371/journal.pdig.0000297},
doi = {10.1371/journal.pdig.0000297},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {PLOS Digital Health},
volume = {3},
number = {2},
pages = {e0000297},
publisher = {Public Library of Science San Francisco, CA USA},
abstract = {Radiology specific clinical decision support systems (CDSS) and artificial intelligence are poorly integrated into the radiologist workflow. Current research and development efforts of radiology CDSS focus on 4 main interventions, based around exam centric time points\textendashafter image acquisition, intra-report support, post-report analysis, and radiology workflow adjacent. We review the literature surrounding CDSS tools in these time points, requirements for CDSS workflow augmentation, and technologies that support clinician to computer workflow augmentation. We develop a theory of radiologist-decision tool interaction using a sequential explanatory study design. The study consists of 2 phases, the first a quantitative survey and the second a qualitative interview study. The phase 1 survey identifies differences between average users and radiologist users in software interventions using the User Acceptance of Information Technology: Toward a Unified View (UTAUT) framework. Phase 2 semi-structured interviews provide narratives on why these differences are found. To build this theory, we propose a novel solution called Radibot\textemdasha conversational agent capable of engaging clinicians with CDSS as an assistant using existing instant messaging systems supporting hospital communications. This work contributes an understanding of how radiologist-users differ from the average user and can be utilized by software developers to increase satisfaction of CDSS tools within radiology.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2023
Venkatayogi, Nethra; Gupta, Maanas; Gupta, Alaukik; Nallaparaju, Shreya; Cheemalamarri, Nithya; Gilari, Krithika; Pathak, Shireen; Vishwanath, Krithik; Soney, Carel; Bhattacharya, Tanisha; others,
From Seeing to Knowing with Artificial Intelligence: A Scoping Review of Point-of-Care Ultrasound in Low-Resource Settings Journal Article
In: Applied Sciences, vol. 13, no. 14, pp. 8427, 2023.
@article{venkatayogi2023seeing,
title = {From Seeing to Knowing with Artificial Intelligence: A Scoping Review of Point-of-Care Ultrasound in Low-Resource Settings},
author = {Nethra Venkatayogi and Maanas Gupta and Alaukik Gupta and Shreya Nallaparaju and Nithya Cheemalamarri and Krithika Gilari and Shireen Pathak and Krithik Vishwanath and Carel Soney and Tanisha Bhattacharya and others},
year = {2023},
date = {2023-07-21},
urldate = {2023-01-01},
journal = {Applied Sciences},
volume = {13},
number = {14},
pages = {8427},
publisher = {MDPI},
abstract = {The utilization of ultrasound imaging for early visualization has been imperative in disease detection, especially in the first responder setting. Over the past decade, rapid advancements in the underlying technology of ultrasound have allowed for the development of portable point-of-care ultrasounds (POCUS) with handheld devices. The application of POCUS is versatile, as seen by its use in pulmonary, cardiovascular, and neonatal imaging, among many others. However, despite these advances, there is an inherent inability of translating POCUS devices to low-resource settings (LRS). To bridge these gaps, the implementation of artificial intelligence offers an interesting opportunity. Our work reviews recent applications of POCUS devices within LRS from 2016 to 2023, identifying the most commonly utilized clinical applications and areas where further innovation is needed. Furthermore, we pinpoint areas of POCUS technologies that can be improved using state-of-art artificial intelligence technologies, thus enabling the widespread adoption of POCUS devices in low-resource settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Merine, Regina; Dsouza, Vyona; Singh, Pallavi; Gichoya, Judy
Evaluating user acceptance of an open-source mobile app for hospital price transparency rule Conference
ECIS 2023 Research Papers, vol. 403, AIS eLibrary, 2023.
@conference{purkayastha2023evaluating,
title = {Evaluating user acceptance of an open-source mobile app for hospital price transparency rule},
author = {Saptarshi Purkayastha and Regina Merine and Vyona Dsouza and Pallavi Singh and Judy Gichoya},
year = {2023},
date = {2023-05-11},
urldate = {2023-05-11},
booktitle = {ECIS 2023 Research Papers},
volume = {403},
publisher = {AIS eLibrary},
abstract = {In 2021, the US Center for Medicare and Medicaid Service mandated the Price Transparency Rule, requiring hospitals to publish a patient service price list called Charge Description Master. However, the mandated machine-readable formats made it difficult for patients to understand pricing and limited price transparency. To address this, we developed the LibreHealth Cost of Care Explorer App to provide patients with a user-friendly format of the CDM. We conducted a mixed-methods user study with 55 patients in two large US cities, one in a safety-net hospital and another in a for-profit hospital, and used PLS-SEM path modeling to analyze the app’s acceptability using the Unified Theory of Acceptance and Use of Technology constructs. Behavioral Intention and Facilitating Conditions significantly impacted Usage Behavior. Effort Expectancy also had a positive impact. Further explanations for the observed model differences in the two hospital systems were obtained from think-aloud observations and semi-structured interviews.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Owosela, Babajide O; Steinberg, Rebecca S; Leslie, Sharon L; Celi, Leo A; Purkayastha, Saptarshi; Shiradkar, Rakesh; Newsome, Janice M; Gichoya, Judy W
Identifying and improving the "ground truth" of race in disparities research through improved EMR data reporting. A systematic review Journal Article
In: International Journal of Medical Informatics, pp. 105303, 2023.
@article{owosela2023identifying,
title = {Identifying and improving the "ground truth" of race in disparities research through improved EMR data reporting. A systematic review},
author = {Babajide O Owosela and Rebecca S Steinberg and Sharon L Leslie and Leo A Celi and Saptarshi Purkayastha and Rakesh Shiradkar and Janice M Newsome and Judy W Gichoya},
doi = {10.1016/j.ijmedinf.2023.105303},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {International Journal of Medical Informatics},
pages = {105303},
publisher = {Elsevier},
abstract = {Background: Studies about racial disparities in healthcare are increasing in quantity; however, they are subject to vast differences in definition, classification, and utilization of race/ethnicity data. Improved standardization of this information can strengthen conclusions drawn from studies using such data. The objective of this study is to examine how data related to race/ethnicity are recorded in research through examining articles on race/ethnicity health disparities and examine problems and solutions in data reporting that may impact overall data quality.
Methods: In this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews.
Findings: In this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies.
Interpretation: Conclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methods: In this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews.
Findings: In this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies.
Interpretation: Conclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.
Kathiravelu, Pradeeban; Fonović, Dalibor; Grbac, Tihana Galinac; Zaiman, Zachary; Veiga, Luís; Gichoya, Judy Wawira; Purkayastha, Saptarshi; Mahmoudi, Babak
The Telehealth Dilemma—Health-Care Deserts Meet the Internet’s Remote Regions Journal Article
In: Computer, vol. 56, no. 9, pp. 39–49, 2023.
@article{kathiravelu2023telehealth,
title = {The Telehealth Dilemma\textemdashHealth-Care Deserts Meet the Internet’s Remote Regions},
author = {Pradeeban Kathiravelu and Dalibor Fonovi\'{c} and Tihana Galinac Grbac and Zachary Zaiman and Lu\'{i}s Veiga and Judy Wawira Gichoya and Saptarshi Purkayastha and Babak Mahmoudi},
year = {2023},
date = {2023-01-01},
journal = {Computer},
volume = {56},
number = {9},
pages = {39\textendash49},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Burns, John Lee; Zaiman, Zachary; Vanschaik, Jack; Luo, Gaoxiang; Peng, Le; Price, Brandon; Mathias, Garric; Mittal, Vijay; Sagane, Akshay; Tignanelli, Christopher; Chakraborty, Sunandan; Gichoya, Judy Wawira; Purkayastha, Saptarshi
Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts Journal Article
In: Journal of Medical Imaging, vol. 10, no. 6, pp. 061106–061106, 2023.
@article{burns2023ability,
title = {Ability of artificial intelligence to identify self-reported race in chest x-ray using pixel intensity counts},
author = {John Lee Burns and Zachary Zaiman and Jack Vanschaik and Gaoxiang Luo and Le Peng and Brandon Price and Garric Mathias and Vijay Mittal and Akshay Sagane and Christopher Tignanelli and Sunandan Chakraborty and Judy Wawira Gichoya and Saptarshi Purkayastha},
doi = {10.1117/1.JMI.10.6.061106},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Journal of Medical Imaging},
volume = {10},
number = {6},
pages = {061106\textendash061106},
publisher = {Society of Photo-Optical Instrumentation Engineers},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Banerjee, Imon; Bhattacharjee, Kamanasish; Burns, John L; Trivedi, Hari; Purkayastha, Saptarshi; Seyyed-Kalantari, Laleh; Patel, Bhavik N; Shiradkar, Rakesh; Gichoya, Judy
“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation. Journal Article
In: Journal of the American College of Radiology, 2023.
@article{banerjee2023shortcuts,
title = {“Shortcuts” causing bias in radiology artificial intelligence: causes, evaluation and mitigation.},
author = {Imon Banerjee and Kamanasish Bhattacharjee and John L Burns and Hari Trivedi and Saptarshi Purkayastha and Laleh Seyyed-Kalantari and Bhavik N Patel and Rakesh Shiradkar and Judy Gichoya},
doi = {10.1016/j.jacr.2023.06.025},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Journal of the American College of Radiology},
publisher = {Elsevier},
abstract = {Despite the expert-level performance of artificial intelligence (AI) models for various medical imaging tasks, real-world performance failures with disparate outputs for various subgroups limit the usefulness of AI in improving patients’ lives. Many definitions of fairness have been proposed, with discussions of various tensions that arise in the choice of an appropriate metric to use to evaluate bias; for example, should one aim for individual or group fairness? One central observation is that AI models apply “shortcut learning” whereby spurious features (such as chest tubes and portable radiographic markers on intensive care unit chest radiography) on medical images are used for prediction instead of identifying true pathology. Moreover, AI has been shown to have a remarkable ability to detect protected attributes of age, sex, and race, while the same models demonstrate bias against historically underserved subgroups of age, sex, and race in disease diagnosis. Therefore, an AI model may take shortcut predictions from these correlations and subsequently generate an outcome that is biased toward certain subgroups even when protected attributes are not explicitly used as inputs into the model. As a result, these subgroups became nonprivileged subgroups. In this review, the authors discuss the various types of bias from shortcut learning that may occur at different phases of AI model development, including data bias, modeling bias, and inference bias. The authors thereafter summarize various tool kits that can be used to evaluate and mitigate bias and note that these have largely been applied to nonmedical domains and require more evaluation for medical AI. The authors then summarize current techniques for mitigating bias from preprocessing (data-centric solutions) and during model development (computational solutions) and postprocessing (recalibration of learning). Ongoing legal changes where the use of a biased model will be penalized highlight the necessity of understanding, detecting, and mitigating biases from shortcut learning and will require diverse research teams looking at the whole AI pipeline.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Paddo, Atika Rahman; Afreen, Sadia; Purkayastha, Saptarshi
Hierarchical Clustering and Multivariate Forecasting for Health Econometrics Proceedings Article
In: epiDAMIK 6.0: The 6th International workshop on Epidemiology meets Data Mining and Knowledge Discovery at KDD 2023, 2023.
@inproceedings{paddo2023hierarchical,
title = {Hierarchical Clustering and Multivariate Forecasting for Health Econometrics},
author = {Atika Rahman Paddo and Sadia Afreen and Saptarshi Purkayastha},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {epiDAMIK 6.0: The 6th International workshop on Epidemiology meets Data Mining and Knowledge Discovery at KDD 2023},
abstract = {Data science approaches in Health Econometrics and Public Health research are limited, with a lack of exploration of state-of-the-art computational methods. Recent studies have shown that neural networks and machine learning methods outperform traditional statistical methods in forecasting and time-series analysis. In this study, we demonstrate the use of unsupervised and supervised machine learning approaches to create "what-if" scenarios for forecasting the long-term impact of changes in socio-economic indicators on health indicators. These indicators include basic sanitation services, immunization, population ages, life expectancy, and domestic health expenditure. To begin, we utilized Hierarchical Cluster Analysis to group 131 countries into 9 clusters based on various indicators from the World Bank Health Statistics and Nutrition dataset. This step allowed us to create clusters of countries. In order to showcase the feasibility of our approach, we performed a time series analysis using multivariate prophet on the most significant features from a cluster consisting of Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia. The study developed robust models (𝑅2 = 0.93+) capable
of forecasting 11 health indicators up to 10 years into the future. By employing these "what-if" scenarios and forecasting models, policymakers and healthcare practitioners can make informed decisions and effectively implement targeted interventions to address health-related challenges.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
of forecasting 11 health indicators up to 10 years into the future. By employing these "what-if" scenarios and forecasting models, policymakers and healthcare practitioners can make informed decisions and effectively implement targeted interventions to address health-related challenges.
Guo, Xiaoyuan; Gichoya, Judy Wawira; Trivedi, Hari; Purkayastha, Saptarshi; Banerjee, Imon
MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation Journal Article
In: IEEE Journal of Biomedical and Health Informatics, 2023.
@article{guo2023medshift,
title = {MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Hari Trivedi and Saptarshi Purkayastha and Imon Banerjee},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {IEEE},
abstract = {Automated curation of noisy external data in the medical domain has long been in high demand, as AI technologies need to be validated using various sources with clean, annotated data. Identifying the variance between internal and external sources is a fundamental step in curating a high-quality dataset, as the data distributions from different sources can vary significantly and subsequently affect the performance of AI models. The primary challenges for detecting data shifts are - (1) accessing private data across healthcare institutions for manual detection and (2) the lack of automated approaches to learn efficient shift-data representation without training samples. To overcome these problems, we propose an automated pipeline called MedShift to detect top-level shift samples and evaluate the significance of shift data without sharing data between internal and external organizations. MedShift employs unsupervised anomaly detectors to learn the internal distribution and identify samples showing significant shiftness for external datasets, and then compares their performance. To quantify the effects of detected shift data, we train a multi-class classifier that learns internal domain knowledge and evaluates the classification performance for each class in external domains after dropping the shift data. We also propose a data quality metric to quantify the dissimilarity between internal and external datasets. We verify the efficacy of MedShift using musculoskeletal radiographs (MURA) and chest X-ray datasets from multiple external sources. Our experiments show that our proposed shift data detection pipeline can be beneficial for medical centers to curate high-quality datasets more efficiently.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Li, Hanzhou; Moon, John T; Purkayastha, Saptarshi; Celi, Leo Anthony; Trivedi, Hari; Gichoya, Judy W
Ethics of large language models in medicine and medical research Journal Article
In: The Lancet Digital Health, vol. 5, no. 6, pp. e333–e335, 2023.
@article{li2023ethics,
title = {Ethics of large language models in medicine and medical research},
author = {Hanzhou Li and John T Moon and Saptarshi Purkayastha and Leo Anthony Celi and Hari Trivedi and Judy W Gichoya},
year = {2023},
date = {2023-01-01},
journal = {The Lancet Digital Health},
volume = {5},
number = {6},
pages = {e333\textendashe335},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Isaac, Rohan; Anthony, Sharon; Shukla, Shikhar; Krupinski, Elizabeth A; Danish, Joshua A; Gichoya, Judy Wawira
A general-purpose AI assistant embedded in an open-source radiology information system Proceedings Article
In: International Conference on Artificial Intelligence in Medicine, pp. 373–377, Springer Nature Switzerland Cham 2023.
@inproceedings{purkayastha2023general,
title = {A general-purpose AI assistant embedded in an open-source radiology information system},
author = {Saptarshi Purkayastha and Rohan Isaac and Sharon Anthony and Shikhar Shukla and Elizabeth A Krupinski and Joshua A Danish and Judy Wawira Gichoya},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {International Conference on Artificial Intelligence in Medicine},
pages = {373\textendash377},
organization = {Springer Nature Switzerland Cham},
abstract = {Radiology AI models have made significant progress in near-human performance or surpassing it. However, AI model’s partnership with human radiologist remains an unexplored challenge due to the lack of health information standards, contextual and workflow differences, and data labeling variations. To overcome these challenges, we integrated an AI model service that uses DICOM standard SR annotations into the OHIF viewer in the open-source LibreHealth Radiology Information Systems (RIS). In this paper, we describe the novel Human-AI partnership capabilities of the platform, including few-shot learning and swarm learning approaches to retrain the AI models continuously. Building on the concept of machine teaching, we developed an active learning strategy within the RIS, so that the human radiologist can enable/disable AI annotations as well as “fix”/relabel the AI annotations. These annotations are then used to retrain the models. This helps establish a partnership between the radiologist user and a user-specific AI model. The weights of these user-specific models are then finally shared between multiple models in a swarm learning approach.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
Merine, Regina; Pinnamraju, Jahnavi; Singh, Darshpreet; Gichoya, Judy W; Purkayastha, Saptarshi
LibreHealth Cost-of-Care Explorer: Mobile Application for Patient-friendly Access to Hospital Chargemasters Proceedings Article
In: 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), pp. 26–29, IEEE 2022.
@inproceedings{merine2022librehealth,
title = {LibreHealth Cost-of-Care Explorer: Mobile Application for Patient-friendly Access to Hospital Chargemasters},
author = {Regina Merine and Jahnavi Pinnamraju and Darshpreet Singh and Judy W Gichoya and Saptarshi Purkayastha},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)},
pages = {26\textendash29},
organization = {IEEE},
abstract = {The Centers for Medicare \& Medicaid Services (CMS) mandated that Charge Description Master (CDM) be available online in a machine-readable format by January 1, 2019. These changes were made to increase availability of hospital price information in order to provide patients with more access to their health information and allow physicians to spend more time with their patients. However, the mandated machine-readable formats make it impossible for a patient to interpret the pricing and hence limit price transparency. To bridge this gap, we developed a mobile application that can be used by patients to reveal the cost of a medical procedure or service before receiving it. Patients are able to compare the costs of medical procedures available in surrounding hospitals by viewing the hospital’s CDM and determine which hospital delivers the best care/medical procedures at the best price. Clinical relevance - The LibreHealth cost-of-care explorer app presents the cost of medical procedures in a consumer-friendly format. It provides patient-friendly cost estimates for medical procedures performed in US hospitals and promotes price transparency to the end users.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sinha, Priyanshu; Gichoya, Judy W; Purkayastha, Saptarshi
Leapfrogging medical ai in low-resource contexts using edge tensor processing unit Proceedings Article
In: 2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT), pp. 67–70, IEEE 2022.
@inproceedings{sinha2022leapfrogging,
title = {Leapfrogging medical ai in low-resource contexts using edge tensor processing unit},
author = {Priyanshu Sinha and Judy W Gichoya and Saptarshi Purkayastha},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)},
pages = {67\textendash70},
organization = {IEEE},
abstract = {With each passing year, the state-of-the-art deep learning neural networks grow larger in size, requiring larger computing and power resources. The high compute resources required by these large networks are alienating the majority of the world population that lives in low-resource settings and lacks the infrastructure to benefit from these advancements in medical AI. Current state-of-the-art medical AI, even with cloud resources, is a bit difficult to deploy in remote areas where we don’t have good internet connectivity. We demonstrate a cost-effective approach to deploying medical AI that could be used in limited resource settings using Edge Tensor Processing Unit (TPU). We trained and optimized a classification model on the Chest X-ray 14 dataset and a segmentation model on the Nerve ultrasound dataset using INT8 Quantization Aware Training. Thereafter, we compiled the optimized models for Edge TPU execution. We find that the inference performance on edge TPUs is 10x faster compared to other embedded devices. The optimized model is 3x and 12x smaller for the classification and segmentation respectively, compared to the full precision model. In summary, we show the potential of Edge TPUs for two medical AI tasks with faster inference times, which could potentially be used in low-resource settings for medical AI-based diagnostics. We finally discuss some potential challenges and limitations of our approach for real-world deployments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Guo, Xiaoyuan; Duan, Jiali; Purkayastha, Saptarshi; Trivedi, Hari; Gichoya, Judy Wawira; Banerjee, Imon
OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System Proceedings Article
In: Proceedings of the 2022 International Conference on Multimedia Retrieval, pp. 11–18, 2022.
@inproceedings{guo2022oscars,
title = {OSCARS: An Outlier-Sensitive Content-Based Radiography Retrieval System},
author = {Xiaoyuan Guo and Jiali Duan and Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya and Imon Banerjee},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Proceedings of the 2022 International Conference on Multimedia Retrieval},
pages = {11\textendash18},
abstract = {Improving the retrieval relevance on noisy datasets is an emerging need for the curation of a large-scale clean dataset in the medical domain. While existing methods can be applied for class-wise retrieval (aka. inter-class), they cannot distinguish the granularity of likeness within the same class (aka. intra-class). The problem is exacerbated on medical external datasets, where noisy samples of the same class are treated equally during training. Our goal is to identify both intra/inter-class similarities for fine-grained retrieval. To achieve this, we propose an Outlier-Sensitive Content-based rAdiologhy Retrieval System (OSCARS), consisting of two steps. First, we train an outlier detector on a clean internal dataset in an unsupervised manner. Then we use the trained detector to generate the anomaly scores on the external dataset, whose distribution will be used to bin intra-class variations. Second, we propose a quadruplet (a, p, nintra, ninter) sampling strategy, where intra-class negatives nintra are sampled from bins of the same class other than the bin anchor a belongs to, while n_inter are randomly sampled from inter-classes. We suggest a weighted metric learning objective to balance the intra and inter-class feature learning. We experimented on two representative public radiography datasets. Experiments show the effectiveness of our approach. The training and evaluation code can be found in https://github.com/XiaoyuanGuo/oscars.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Sinha, Priyanshu; Tummala, Sai Sreya; Purkayastha, Saptarshi; Gichoya, Judy
Energy Efficiency of Quantized Neural Networks in Medical Imaging Proceedings Article
In: Medical Imaging with Deep Learning, 2022.
@inproceedings{sinha2022energy,
title = {Energy Efficiency of Quantized Neural Networks in Medical Imaging},
author = {Priyanshu Sinha and Sai Sreya Tummala and Saptarshi Purkayastha and Judy Gichoya},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Medical Imaging with Deep Learning},
abstract = {The main goal of this paper is to compare the energy efficiency of quantized neural networks to perform medical image analysis on different processors and neural network architectures. Deep neural networks have demonstrated outstanding performance in medical image analysis but require high computation and power usage. In our work, we review the power usage and temperature of processors when running Resnet and UNet architectures to perform image classification and segmentation respectively. We compare Edge TPU, Jetson Nano, Apple M1, Nvidia Quadro P6000 and Nvidia A6000 to infer using full-precision FP32 and quantized INT8 models. The results will be useful for designers and implementers of medical imaging AI on hand-held or edge computing devices.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tummala, Sriharsha; Purkayastha, Saptarshi; Jones, Josette
Development and evaluation of a natural language conversational bot for identifying appropriate clinician referral from patient narratives Journal Article
In: 2022.
@article{tummala2022development,
title = {Development and evaluation of a natural language conversational bot for identifying appropriate clinician referral from patient narratives},
author = {Sriharsha Tummala and Saptarshi Purkayastha and Josette Jones},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
abstract = {Recent years have seen a significant increase in automated conversational agent chatbots. Conversational agents like chatbots for health may provide timely and cost-effective support in clinical care. Some studies show that chatbots could have an impact on patient engagement. Additionally, health systems are attempting to connect with patients over social networks, mainly where specialists are limited. By 2025, the Association of American Medical Colleges estimates that the United States will have a shortfall of 61,700-94,700 physicians and critical shortage in many specialties, delaying available appointments by months in many cases. Thus, we need innovative solutions that can manage the time of limited specialists appropriately. Recent research has demonstrated that deep learning methods are superior for natural language classification tasks compared to other machine learning methods. The primary objective of this study was to develop a telegram chatbot which reads patient narratives and acts as a conversational agent by redirecting the case to the appropriate specialist. Besides simply working on improving conversational capabilities of chatbots, we developed a novel method for referring the cases to specialists based on their responses to previous cases on a social network group. As far as we know, no other chatbot has the level of accuracy or referral system like our developed chatbot.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gichoya, Judy Wawira; Banerjee, Imon; Bhimireddy, Ananth Reddy; Burns, John L; Celi, Leo Anthony; Chen, Li-Ching; Correa, Ramon; Dullerud, Natalie; Ghassemi, Marzyeh; Huang, Shih-Cheng; others,
AI recognition of patient race in medical imaging: a modelling study Journal Article
In: The Lancet Digital Health, vol. 4, no. 6, pp. e406–e414, 2022.
@article{gichoya2022ai,
title = {AI recognition of patient race in medical imaging: a modelling study},
author = {Judy Wawira Gichoya and Imon Banerjee and Ananth Reddy Bhimireddy and John L Burns and Leo Anthony Celi and Li-Ching Chen and Ramon Correa and Natalie Dullerud and Marzyeh Ghassemi and Shih-Cheng Huang and others},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {The Lancet Digital Health},
volume = {4},
number = {6},
pages = {e406\textendashe414},
publisher = {Elsevier},
abstract = {Background
Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person\'s race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient\'s racial identity from medical images.
Methods
Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.
Findings
In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91\textendash0·99], CT chest imaging [0·87\textendash0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study.
Interpretation
The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Previous studies in medical imaging have shown disparate abilities of artificial intelligence (AI) to detect a person's race, yet there is no known correlation for race on medical imaging that would be obvious to human experts when interpreting the images. We aimed to conduct a comprehensive evaluation of the ability of AI to recognise a patient's racial identity from medical images.
Methods
Using private (Emory CXR, Emory Chest CT, Emory Cervical Spine, and Emory Mammogram) and public (MIMIC-CXR, CheXpert, National Lung Cancer Screening Trial, RSNA Pulmonary Embolism CT, and Digital Hand Atlas) datasets, we evaluated, first, performance quantification of deep learning models in detecting race from medical images, including the ability of these models to generalise to external environments and across multiple imaging modalities. Second, we assessed possible confounding of anatomic and phenotypic population features by assessing the ability of these hypothesised confounders to detect race in isolation using regression models, and by re-evaluating the deep learning models by testing them on datasets stratified by these hypothesised confounding variables. Last, by exploring the effect of image corruptions on model performance, we investigated the underlying mechanism by which AI models can recognise race.
Findings
In our study, we show that standard AI deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities, which was sustained under external validation conditions (x-ray imaging [area under the receiver operating characteristics curve (AUC) range 0·91–0·99], CT chest imaging [0·87–0·96], and mammography [0·81]). We also showed that this detection is not due to proxies or imaging-related surrogate covariates for race (eg, performance of possible confounders: body-mass index [AUC 0·55], disease distribution [0·61], and breast density [0·61]). Finally, we provide evidence to show that the ability of AI deep learning models persisted over all anatomical regions and frequency spectrums of the images, suggesting the efforts to control this behaviour when it is undesirable will be challenging and demand further study.
Interpretation
The results from our study emphasise that the ability of AI deep learning models to predict self-reported race is itself not the issue of importance. However, our finding that AI can accurately predict self-reported race, even from corrupted, cropped, and noised medical images, often when clinical experts cannot, creates an enormous risk for all model deployments in medical imaging.
Ochoa, Rodrigo; Álvarez, Alessa; Freitas, Jordan; Purkayastha, Saptarshi; Vélez, Iván D
NTD Health: An electronic medical record system for neglected tropical diseases Journal Article
In: Biomédica, vol. 42, no. 4, pp. 602–610, 2022.
@article{ochoa2022ntd,
title = {NTD Health: An electronic medical record system for neglected tropical diseases},
author = {Rodrigo Ochoa and Alessa \'{A}lvarez and Jordan Freitas and Saptarshi Purkayastha and Iv\'{a}n D V\'{e}lez},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {Biom\'{e}dica},
volume = {42},
number = {4},
pages = {602\textendash610},
publisher = {Instituto Nacional de Salud},
abstract = {Introduction:
The use of technological resources to support processes in health systems has generated robust, interoperable, and dynamic platforms. In the case of institutions working with neglected tropical diseases, there is a need for specific customizations of these diseases.
Objectives:
To establish a medical record platform specialized in neglected tropical diseases which could facilitate the analysis of treatment evolution in patients, as well as generate more accurate data about various clinical aspects.
Materials and methods:
A set of requirements to develop state of the art forms, concepts, and functionalities to include neglected tropical diseases were compiled. An OpenMRS distribution (version 2.3) was used as reference to build the platform, following the recommended guidelines and shared-community modules.
Results:
All the customized information was developed in a platform called NTD Health, which is web-based and can be upgraded and improved by users without technological barriers.
Conclusions:
The electronic medical record system can become a useful tool for other institutions to improve their health practices as well as the quality of life for neglected tropical disease patients, simplifying the customization of healthcare systems able to interoperate with other platforms.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The use of technological resources to support processes in health systems has generated robust, interoperable, and dynamic platforms. In the case of institutions working with neglected tropical diseases, there is a need for specific customizations of these diseases.
Objectives:
To establish a medical record platform specialized in neglected tropical diseases which could facilitate the analysis of treatment evolution in patients, as well as generate more accurate data about various clinical aspects.
Materials and methods:
A set of requirements to develop state of the art forms, concepts, and functionalities to include neglected tropical diseases were compiled. An OpenMRS distribution (version 2.3) was used as reference to build the platform, following the recommended guidelines and shared-community modules.
Results:
All the customized information was developed in a platform called NTD Health, which is web-based and can be upgraded and improved by users without technological barriers.
Conclusions:
The electronic medical record system can become a useful tool for other institutions to improve their health practices as well as the quality of life for neglected tropical disease patients, simplifying the customization of healthcare systems able to interoperate with other platforms.
Merine, Regina; Purkayastha, Saptarshi
Risks and Benefits of AI-generated Text Summarization for Expert Level Content in Graduate Health Informatics Proceedings Article
In: 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), pp. 567–574, IEEE 2022.
@inproceedings{merine2022risks,
title = {Risks and Benefits of AI-generated Text Summarization for Expert Level Content in Graduate Health Informatics},
author = {Regina Merine and Saptarshi Purkayastha},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 IEEE 10th International Conference on Healthcare Informatics (ICHI)},
pages = {567\textendash574},
organization = {IEEE},
abstract = {AI-generated text summarization (AI-GTS) is now a popular topic in applied computer science education. It has proven helpful in various sectors, but its benefits and risks in education have not been thoroughly investigated. Few researchers have demonstrated the benefits of employing AI-generated text summaries in learning to generate ideas swiftly and to explore insights and hidden knowledge. AI-GTS has made it easier for students to understand electronically-available critical information. On the other hand, the risks linked with its implementation in education are understudied. Some anticipated risks include harming pupils\' writing skills, overdependence, reduced critical thinking capacity, and increased plagiarism. This paper presents the application of AI-generated text summarization in a graduate health informatics course and discusses the risks and benefits to students. Furthermore, utilizing the Bidirectional Encoder Representations from Transformers (BERT) model, we demonstrate that the current state-of-the-art AI-generated text summarization has the potential to create expert knowledge content. We conducted a study with 58 health informatics graduate students in the Fall of 2019 to write annotated bibliography for 25 articles each, to which we also added the AI-generated article summaries. We then asked the students to peer grade and distinguish the AI-generated annotations from the student-written summary. Using the Kruskal-Wallis test, we found no significant difference in the peer grades between the two. The robustness of such AI-generated text summarization raises important questions for educators teaching in health informatics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ezenwa, Beatrice Nkolika; Umoren, Rachel; Fajolu, Iretiola Bamikeolu; Hippe, Daniel S; Bucher, Sherri; Purkayastha, Saptarshi; Okwako, Felicitas; Esamai, Fabian; Feltner, John B; Olawuyi, Olubukola; others,
Using mobile virtual reality simulation to prepare for in-person helping babies breathe training: Secondary analysis of a randomized controlled trial (the eHBB/mHBS trial) Journal Article
In: JMIR Medical Education, vol. 8, no. 3, pp. e37297, 2022.
@article{ezenwa2022using,
title = {Using mobile virtual reality simulation to prepare for in-person helping babies breathe training: Secondary analysis of a randomized controlled trial (the eHBB/mHBS trial)},
author = {Beatrice Nkolika Ezenwa and Rachel Umoren and Iretiola Bamikeolu Fajolu and Daniel S Hippe and Sherri Bucher and Saptarshi Purkayastha and Felicitas Okwako and Fabian Esamai and John B Feltner and Olubukola Olawuyi and others},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {JMIR Medical Education},
volume = {8},
number = {3},
pages = {e37297},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: Neonatal mortality accounts for approximately 46% of global under-5 child mortality. The widespread access to mobile devices in low- and middle-income countries has enabled innovations, such as mobile virtual reality (VR), to be leveraged in simulation education for health care workers.
Objective: This study explores the feasibility and educational efficacy of using mobile VR for the precourse preparation of health care professionals in neonatal resuscitation training.
Methods: Health care professionals in obstetrics and newborn care units at 20 secondary and tertiary health care facilities in Lagos, Nigeria, and Busia, Western Kenya, who had not received training in Helping Babies Breathe (HBB) within the past 1 year were randomized to access the electronic HBB VR simulation and digitized HBB Provider\'s Guide (VR group) or the digitized HBB Provider\'s Guide only (control group). A sample size of 91 participants per group was calculated based on the main study protocol that was previously published. Participants were directed to use the electronic HBB VR simulation and digitized HBB Provider\'s Guide or the digitized HBB Provider\'s Guide alone for a minimum of 20 minutes. HBB knowledge and skills assessments were then conducted, which were immediately followed by a standard, in-person HBB training course that was led by study staff and used standard HBB evaluation tools and the Neonatalie Live manikin (Laerdal Medical).
Results: A total of 179 nurses and midwives participated (VR group: n=91; control group: n=88). The overall performance scores on the knowledge check (P=.29), bag and mask ventilation skills check (P=.34), and Objective Structured Clinical Examination A checklist (P=.43) were similar between groups, with low overall pass rates (6/178, 3.4% of participants). During the Objective Structured Clinical Examination A test, participants in the VR group performed better on the critical step of positioning the head and clearing the airway (VR group: 77/90, 86%; control group: 57/88, 65%; P=.002). The median percentage of ventilations that were performed via head tilt, as recorded by the Neonatalie Live manikin, was also numerically higher in the VR group (75%, IQR 9%-98%) than in the control group (62%, IQR 13%-97%), though not statistically significantly different (P=.35). Participants in the control group performed better on the identifying a helper and reviewing the emergency plan step (VR group: 7/90, 8%; control group: 16/88, 18%; P=.045) and the washing hands step (VR group: 20/90, 22%; control group: 32/88, 36%; P=.048).
Conclusions: The use of digital interventions, such as mobile VR simulations, may be a viable approach to precourse preparation in neonatal resuscitation training for health care professionals in low- and middle-income countries.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective: This study explores the feasibility and educational efficacy of using mobile VR for the precourse preparation of health care professionals in neonatal resuscitation training.
Methods: Health care professionals in obstetrics and newborn care units at 20 secondary and tertiary health care facilities in Lagos, Nigeria, and Busia, Western Kenya, who had not received training in Helping Babies Breathe (HBB) within the past 1 year were randomized to access the electronic HBB VR simulation and digitized HBB Provider's Guide (VR group) or the digitized HBB Provider's Guide only (control group). A sample size of 91 participants per group was calculated based on the main study protocol that was previously published. Participants were directed to use the electronic HBB VR simulation and digitized HBB Provider's Guide or the digitized HBB Provider's Guide alone for a minimum of 20 minutes. HBB knowledge and skills assessments were then conducted, which were immediately followed by a standard, in-person HBB training course that was led by study staff and used standard HBB evaluation tools and the Neonatalie Live manikin (Laerdal Medical).
Results: A total of 179 nurses and midwives participated (VR group: n=91; control group: n=88). The overall performance scores on the knowledge check (P=.29), bag and mask ventilation skills check (P=.34), and Objective Structured Clinical Examination A checklist (P=.43) were similar between groups, with low overall pass rates (6/178, 3.4% of participants). During the Objective Structured Clinical Examination A test, participants in the VR group performed better on the critical step of positioning the head and clearing the airway (VR group: 77/90, 86%; control group: 57/88, 65%; P=.002). The median percentage of ventilations that were performed via head tilt, as recorded by the Neonatalie Live manikin, was also numerically higher in the VR group (75%, IQR 9%-98%) than in the control group (62%, IQR 13%-97%), though not statistically significantly different (P=.35). Participants in the control group performed better on the identifying a helper and reviewing the emergency plan step (VR group: 7/90, 8%; control group: 16/88, 18%; P=.045) and the washing hands step (VR group: 20/90, 22%; control group: 32/88, 36%; P=.048).
Conclusions: The use of digital interventions, such as mobile VR simulations, may be a viable approach to precourse preparation in neonatal resuscitation training for health care professionals in low- and middle-income countries.
Guo, Xiaoyuan; Gichoya, Judy Wawira; Purkayastha, Saptarshi; Banerjee, Imon
CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE Proceedings Article
In: Workshop on Medical Image Learning with Limited and Noisy Data, pp. 187–196, Springer Nature Switzerland Cham 2022.
@inproceedings{guo2022cvad,
title = {CVAD: An Anomaly Detector for Medical Images Based on Cascade VAE},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {Workshop on Medical Image Learning with Limited and Noisy Data},
pages = {187\textendash196},
organization = {Springer Nature Switzerland Cham},
abstract = {Anomaly detection in medical imaging plays an important role to ensure AI generalization. However, existing out-of-distribution (OOD) detection approaches fail to account for OOD data granularity in medical images, where identifying both intra-class and inter-class OOD data is essential to the generalizability in the medical domain. We focus on the generalizability of outlier detection for medical images and propose a generic Cascade Variational autoencoder-based Anomaly Detector (CVAD). We use variational autoencoders’ cascade architecture, which combines latent representation at multiple scales, before being fed to a discriminator to distinguish the OOD data from the in-distribution data. Finally, both the reconstruction error and the OOD probability predicted by the binary discriminator are used to determine the anomalies. We compare the performance with the state-of-the-art deep learning models to demonstrate our model’s efficacy on various open-access natural and medical imaging datasets for intra- and inter-class OOD. Extensive experimental results on multiple datasets show our model’s effectiveness and generalizability. The code will be publicly available.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Guo, Xiaoyuan; Duan, Jiali; Gichoya, Judy; Trivedi, Hari; Purkayastha, Saptarshi; Sharma, Ashish; Banerjee, Imon
Multi-label Medical Image Retrieval via Learning Multi-class Similarity Journal Article
In: 2022.
@article{guo2022multi,
title = {Multi-label Medical Image Retrieval via Learning Multi-class Similarity},
author = {Xiaoyuan Guo and Jiali Duan and Judy Gichoya and Hari Trivedi and Saptarshi Purkayastha and Ashish Sharma and Imon Banerjee},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {AIIM-D-22-00928},
abstract = {Introduction: Multi-label image retrieval is a challenging problem in the medical area. First, compared to natural images, labels in the medical domain exhibit higher class-imbalance and much nuanced variations. Second, pair-based sampling for positives and negatives during similarity optimization are ambiguous in the multi-label setting, as samples with the same set of labels are limited.
Methods: To address the aforementioned challenges, we propose a proxy-based multi-class similarity (PMS) framework, which compares and contrasts samples by comparing their similarities with the discovered proxies. In this way, samples of different sets of label attributes can be utilized and compared indirectly, without the need for complicated sampling. PMS learns a class-wise feature decomposition and maintains a memory bank for positive features from each class. The memory bank keeps track of the latest features, used to compute the class proxies. We compare samples based on their similarity distributions against the proxies, which provide a more stable mean against noise.
Results: We benchmark over 10 popular metric learning baselines on two public chest X-ray datasets and experiments show consistent stability of our approach under both exact and non-exact match settings.
Conclusions: We proposed a methodology for multi-label medical image retrieval and design a proxy-based multi-class similarity metric, which compares and contrasts samples based on their similarity distributions with respect to the class proxies. With no perquisites, the metrics can be applied to various multi-label medical image applications. The implementation code repository will be publicly available after acceptance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methods: To address the aforementioned challenges, we propose a proxy-based multi-class similarity (PMS) framework, which compares and contrasts samples by comparing their similarities with the discovered proxies. In this way, samples of different sets of label attributes can be utilized and compared indirectly, without the need for complicated sampling. PMS learns a class-wise feature decomposition and maintains a memory bank for positive features from each class. The memory bank keeps track of the latest features, used to compute the class proxies. We compare samples based on their similarity distributions against the proxies, which provide a more stable mean against noise.
Results: We benchmark over 10 popular metric learning baselines on two public chest X-ray datasets and experiments show consistent stability of our approach under both exact and non-exact match settings.
Conclusions: We proposed a methodology for multi-label medical image retrieval and design a proxy-based multi-class similarity metric, which compares and contrasts samples based on their similarity distributions with respect to the class proxies. With no perquisites, the metrics can be applied to various multi-label medical image applications. The implementation code repository will be publicly available after acceptance.
Kathiravelu, Pradeeban; Benkhelifa, Elhadj; Zaiman, Zachary; Wang, Matthew; Correa, Ramon; Veiga, Luís; Banerjee, Imon; Trivedi, Hari; Purkayastha, Saptarshi; Gichoya, Judy; others,
Networking Research Innovations for Telesurgery: A Systematic Review Proceedings Article
In: 2022 Ninth International Conference on Software Defined Systems (SDS), pp. 1–8, IEEE 2022.
@inproceedings{kathiravelu2022networking,
title = {Networking Research Innovations for Telesurgery: A Systematic Review},
author = {Pradeeban Kathiravelu and Elhadj Benkhelifa and Zachary Zaiman and Matthew Wang and Ramon Correa and Lu\'{i}s Veiga and Imon Banerjee and Hari Trivedi and Saptarshi Purkayastha and Judy Gichoya and others},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 Ninth International Conference on Software Defined Systems (SDS)},
pages = {1\textendash8},
organization = {IEEE},
abstract = {Networking is a core enabler of telesurgery, which has used networking research innovations for various surgical sub-specialties and geographical regions. This paper systematically reviews how networking research innovations have explicitly been carried out to improve telesurgery performance and summarizes future lines of research and innovation. We used networking (or networks) and telesurgery (or remote surgery) as our inclusion criteria, excluding non-computer networks such as healthcare networks. Our initial screening on IEEE, PubMed, and Scopus for networking research innovations aimed at telesurgery yielded 17,854 studies. We extracted data from 15 papers after the title, abstract, and full-text screening of the identified studies. A review of the 15 studies shows innovations in networking protocols, integration with connectivity providers, distributed systems, and adaptive approaches to enhance telesurgery. These innovative approaches optimize telesurgery applications across medical domains in research and practice. Recently, research has also focused on how telesurgery networks can be made more flexible through approaches such as Software-Defined Networking (SDN) and Network Functions Virtualization (NFV). We also note the rise in networking research innovations in recent years compared to the past. Networking has undergone several innovations to enhance bandwidth while minimizing latency, latency variations (jitter), and data loss. Networking research innovations have been leveraged locally in many countries and between nations, facilitating specialist care in remote regions that lack healthcare access. We foresee that networking research innovations will continue to revolutionize telesurgery applications and reach in the future.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2021
Guo, Xiaoyuan; Gichoya, Judy Wawira; Purkayastha, Saptarshi; Banerjee, Imon
CVAD/An unsupervised image anomaly detector Journal Article
In: Software Impacts, pp. 100195, 2021.
@article{guo2021cvad,
title = {CVAD/An unsupervised image anomaly detector},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},
doi = {10.1016/j.simpa.2021.100195},
year = {2021},
date = {2021-12-22},
urldate = {2021-01-01},
journal = {Software Impacts},
pages = {100195},
publisher = {Elsevier},
abstract = {Detecting out-of-distribution samples for image applications plays an important role in safeguarding the reliability of machine learning model deployment. In this article, we developed a software tool to support our OOD detector CVAD - a self-supervised Cascade Variational autoencoder-based Anomaly Detector , which can be easily applied to various image applications without any assumptions. The corresponding open-source software is published for better public research and tool usage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mahajan, Yohan; Bhimireddy, Ananth; Abid, Areeba; Gichoya, Judy W; Purkayastha, Saptarshi
PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor Journal Article
In: Data in Brief, vol. 38, pp. 107287, 2021.
@article{mahajan2021plhi,
title = {PLHI-MC10: A dataset of exercise activities captured through a triple synchronous medically-approved sensor},
author = {Yohan Mahajan and Ananth Bhimireddy and Areeba Abid and Judy W Gichoya and Saptarshi Purkayastha},
doi = {10.1016/j.dib.2021.107287},
year = {2021},
date = {2021-10-01},
urldate = {2021-01-01},
journal = {Data in Brief},
volume = {38},
pages = {107287},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Abid, Areeba; Sinha, Priyanshu; Harpale, Aishwarya; Gichoya, Judy; Purkayastha, Saptarshi
Optimizing Medical Image Classification Models for Edge Devices Proceedings Article
In: International Symposium on Distributed Computing and Artificial Intelligence, pp. 77–87, Springer, Cham 2021, ISBN: 978-3-030-86261-9.
@inproceedings{abid2021optimizing,
title = {Optimizing Medical Image Classification Models for Edge Devices},
author = {Areeba Abid and Priyanshu Sinha and Aishwarya Harpale and Judy Gichoya and Saptarshi Purkayastha},
doi = {10.1007/978-3-030-86261-9_8},
isbn = {978-3-030-86261-9},
year = {2021},
date = {2021-09-02},
urldate = {2021-01-01},
booktitle = {International Symposium on Distributed Computing and Artificial Intelligence},
pages = {77--87},
organization = {Springer, Cham},
abstract = {Machine learning algorithms for medical diagnostics often require resource-intensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2\textendash4x reduction in model size, offset by small decreases in the mean AUC-ROC score of 0.0%\textendash0.9%. On ARM architectures, integer quantization was shown to improve inference latency by up to 57%. However, we also observe significant increases in latency on x86 processors. GPU acceleration also improved inference latency, but this was outweighed by kernel launch overhead. We show that optimization of diagnostic models has the potential to expand their utility to day-to-day devices used by patients and healthcare workers; however, these improvements are context- and architecture-dependent and should be tested on the relevant devices before deployment in low-resource environments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Umoren, Rachel; Bucher, Sherri; Hippe, Daniel S; Ezenwa, Beatrice Nkolika; Fajolu, Iretiola Bamikeolu; Okwako, Felicitas M; Feltner, John; Nafula, Mary; Musale, Annet; Olawuyi, Olubukola A; others,
In: BMJ open, vol. 11, no. 8, pp. e048506, 2021.
@article{umoren2021ehbbb,
title = {eHBB: a randomised controlled trial of virtual reality or video for neonatal resuscitation refresher training in healthcare workers in resource-scarce settings},
author = {Rachel Umoren and Sherri Bucher and Daniel S Hippe and Beatrice Nkolika Ezenwa and Iretiola Bamikeolu Fajolu and Felicitas M Okwako and John Feltner and Mary Nafula and Annet Musale and Olubukola A Olawuyi and others},
doi = {10.1136/bmjopen-2020-048506},
year = {2021},
date = {2021-08-25},
urldate = {2021-01-01},
journal = {BMJ open},
volume = {11},
number = {8},
pages = {e048506},
publisher = {British Medical Journal Publishing Group},
abstract = {Objective To assess the impact of mobile virtual reality (VR) simulations using electronic Helping Babies Breathe (eHBB) or video for the maintenance of neonatal resuscitation skills in healthcare workers in resource-scarce settings.
Design Randomised controlled trial with 6-month follow-up (2018\textendash2020).
Setting Secondary and tertiary healthcare facilities.
Participants 274 nurses and midwives assigned to labour and delivery, operating room and newborn care units were recruited from 20 healthcare facilities in Nigeria and Kenya and randomised to one of three groups: VR (eHBB+digital guide), video (video+digital guide) or control (digital guide only) groups before an in-person HBB course.
Intervention(s) eHBB VR simulation or neonatal resuscitation video.
Main outcome(s) Healthcare worker neonatal resuscitation skills using standardised checklists in a simulated setting at 1 month, 3 months and 6 months.
Results Neonatal resuscitation skills pass rates were similar among the groups at 6-month follow-up for bag-and-mask ventilation (BMV) skills check (VR 28%, video 25%, control 22%, p=0.71), objective structured clinical examination (OSCE) A (VR 76%, video 76%, control 72%, p=0.78) and OSCE B (VR 62%, video 60%, control 49%, p=0.18). Relative to the immediate postcourse assessments, there was greater retention of BMV skills at 6 months in the VR group (−15% VR, p=0.10; −21% video, p\<0.01, \textendash27% control, p=0.001). OSCE B pass rates in the VR group were numerically higher at 3 months (+4%, p=0.64) and 6 months (+3%, p=0.74) and lower in the video (−21% at 3 months, p\<0.001; −14% at 6 months, p=0.066) and control groups (−7% at 3 months, p=0.43; −14% at 6 months, p=0.10). On follow-up survey, 95% (n=65) of respondents in the VR group and 98% (n=82) in the video group would use their assigned intervention again.
Conclusion eHBB VR training was highly acceptable to healthcare workers in low-income to middle-income countries and may provide additional support for neonatal resuscitation skills retention compared with other digital interventions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Design Randomised controlled trial with 6-month follow-up (2018–2020).
Setting Secondary and tertiary healthcare facilities.
Participants 274 nurses and midwives assigned to labour and delivery, operating room and newborn care units were recruited from 20 healthcare facilities in Nigeria and Kenya and randomised to one of three groups: VR (eHBB+digital guide), video (video+digital guide) or control (digital guide only) groups before an in-person HBB course.
Intervention(s) eHBB VR simulation or neonatal resuscitation video.
Main outcome(s) Healthcare worker neonatal resuscitation skills using standardised checklists in a simulated setting at 1 month, 3 months and 6 months.
Results Neonatal resuscitation skills pass rates were similar among the groups at 6-month follow-up for bag-and-mask ventilation (BMV) skills check (VR 28%, video 25%, control 22%, p=0.71), objective structured clinical examination (OSCE) A (VR 76%, video 76%, control 72%, p=0.78) and OSCE B (VR 62%, video 60%, control 49%, p=0.18). Relative to the immediate postcourse assessments, there was greater retention of BMV skills at 6 months in the VR group (−15% VR, p=0.10; −21% video, p<0.01, –27% control, p=0.001). OSCE B pass rates in the VR group were numerically higher at 3 months (+4%, p=0.64) and 6 months (+3%, p=0.74) and lower in the video (−21% at 3 months, p<0.001; −14% at 6 months, p=0.066) and control groups (−7% at 3 months, p=0.43; −14% at 6 months, p=0.10). On follow-up survey, 95% (n=65) of respondents in the VR group and 98% (n=82) in the video group would use their assigned intervention again.
Conclusion eHBB VR training was highly acceptable to healthcare workers in low-income to middle-income countries and may provide additional support for neonatal resuscitation skills retention compared with other digital interventions.
Kathiravelu, Pradeeban; Sharma, Puneet; Sharma, Ashish; Banerjee, Imon; Trivedi, Hari; Purkayastha, Saptarshi; Sinha, Priyanshu; Cadrin-Chenevert, Alexandre; Safdar, Nabile; Gichoya, Judy Wawira
A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images Journal Article
In: Journal of Digital Imaging, vol. 34, no. 4, pp. 1005–1013, 2021.
@article{kathiravelu2021dicom,
title = {A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images},
author = {Pradeeban Kathiravelu and Puneet Sharma and Ashish Sharma and Imon Banerjee and Hari Trivedi and Saptarshi Purkayastha and Priyanshu Sinha and Alexandre Cadrin-Chenevert and Nabile Safdar and Judy Wawira Gichoya},
doi = {10.1007/s10278-021-00491-w},
year = {2021},
date = {2021-08-01},
urldate = {2021-01-01},
journal = {Journal of Digital Imaging},
volume = {34},
number = {4},
pages = {1005--1013},
publisher = {Springer International Publishing},
abstract = {Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Guo, Xiaoyuan; Gichoya, Judy Wawira; Purkayastha, Saptarshi; Banerjee, Imon
Margin-Aware Intra-Class Novelty Identification for Medical Images Journal Article
In: arXiv preprint arXiv:2108.00117, 2021.
@article{guo2021margin,
title = {Margin-Aware Intra-Class Novelty Identification for Medical Images},
author = {Xiaoyuan Guo and Judy Wawira Gichoya and Saptarshi Purkayastha and Imon Banerjee},
url = {https://arxiv.org/abs/2108.00117},
year = {2021},
date = {2021-07-31},
urldate = {2021-07-31},
journal = {arXiv preprint arXiv:2108.00117},
abstract = {Traditional anomaly detection methods focus on detecting inter-class variations while medical image novelty identification is inherently an intra-class detection problem. For example, a machine learning model trained with normal chest X-ray and common lung abnormalities, is expected to discover and flag idiopathic pulmonary fibrosis which a rare lung disease and unseen by the model during training. The nuances from intra-class variations and lack of relevant training data in medical image analysis pose great challenges for existing anomaly detection methods. To tackle the challenges, we propose a hybrid model - Transformation-based Embedding learning for Novelty Detection (TEND) which without any out-of-distribution training data, performs novelty identification by combining both autoencoder-based and classifier-based method. With a pre-trained autoencoder as image feature extractor, TEND learns to discriminate the feature embeddings of in-distribution data from the transformed counterparts as fake out-of-distribution inputs. To enhance the separation, a distance objective is optimized to enforce a margin between the two classes. Extensive experimental results on both natural image datasets and medical image datasets are presented and our method out-performs state-of-the-art approaches. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Banerjee, Imon; Bhimireddy, Ananth Reddy; Burns, John L; Celi, Leo Anthony; Chen, Li-Ching; Correa, Ramon; Dullerud, Natalie; Ghassemi, Marzyeh; Huang, Shih-Cheng; Kuo, Po-Chih; others,
Reading Race: AI Recognises Patient’s Racial Identity In Medical Images Journal Article
In: arXiv preprint arXiv:2107.10356, 2021.
@article{banerjee2021reading,
title = {Reading Race: AI Recognises Patient's Racial Identity In Medical Images},
author = {Imon Banerjee and Ananth Reddy Bhimireddy and John L Burns and Leo Anthony Celi and Li-Ching Chen and Ramon Correa and Natalie Dullerud and Marzyeh Ghassemi and Shih-Cheng Huang and Po-Chih Kuo and others},
url = {https://arxiv.org/abs/2107.10356},
year = {2021},
date = {2021-07-21},
urldate = {2021-07-21},
journal = {arXiv preprint arXiv:2107.10356},
abstract = {Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images.
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study.
Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race -- even from corrupted, cropped, and noised medical images -- in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
Findings: Standard deep learning models can be trained to predict race from medical images with high performance across multiple imaging modalities. Our findings hold under external validation conditions, as well as when models are optimized to perform clinically motivated tasks. We demonstrate this detection is not due to trivial proxies or imaging-related surrogate covariates for race, such as underlying disease distribution. Finally, we show that performance persists over all anatomical regions and frequency spectrum of the images suggesting that mitigation efforts will be challenging and demand further study.
Interpretation: We emphasize that model ability to predict self-reported race is itself not the issue of importance. However, our findings that AI can trivially predict self-reported race — even from corrupted, cropped, and noised medical images — in a setting where clinical experts cannot, creates an enormous risk for all model deployments in medical imaging: if an AI model secretly used its knowledge of self-reported race to misclassify all Black patients, radiologists would not be able to tell using the same data the model has access to.
Kodela, Snigdha; Pinnamraju, Jahnavi; Gichoya, Judy W; Purkayastha, Saptarshi
Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV Proceedings Article
In: 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), pp. 225–230, IEEE 2021.
@inproceedings{kodela2021predicting,
title = {Predicting Opioid Prescriptions based on Patient Demographics in MIMIC-IV},
author = {Snigdha Kodela and Jahnavi Pinnamraju and Judy W Gichoya and Saptarshi Purkayastha},
doi = {10.1109/CBMS52027.2021.00023},
year = {2021},
date = {2021-06-07},
urldate = {2021-01-01},
booktitle = {2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {225--230},
organization = {IEEE},
abstract = {Opioids are widely used analgesics because of their efficacy, mild sedative and anxiolytic properties, and flexibility to administer through multiple routes. Understanding the demographics of the patients receiving these medications helps provide customized care for the susceptible group of people. We conducted a demographic evaluation of the frequently prescribed opioid drug prescriptions from the MIMIC IV database. We analyzed prescribing patterns of six commonly used opioids with demographics such as age, gender, ethnicity, marital status, and year predominantly. After conducting exploratory data analysis, we built models using Logistic Regression, Random Forest, and XGBoost to predict opioid prescriptions and demographics for those. We also analyzed the association between demographics and the frequency of prescribed medications for pain management. We found statistically significant differences in opioid prescriptions among the male and female population, married and unmarried, various ages, ethnic groups, and an association with in-hospital deaths.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Tariq, Amara; Celi, Leo Anthony; Newsome, Janice M; Purkayastha, Saptarshi; Bhatia, Neal Kumar; Trivedi, Hari; Gichoya, Judy Wawira; Banerjee, Imon
Patient-specific COVID-19 resource utilization prediction using fusion AI model Journal Article
In: NPJ digital medicine, vol. 4, no. 1, pp. 1–9, 2021.
@article{tariq2021patient,
title = {Patient-specific COVID-19 resource utilization prediction using fusion AI model},
author = {Amara Tariq and Leo Anthony Celi and Janice M Newsome and Saptarshi Purkayastha and Neal Kumar Bhatia and Hari Trivedi and Judy Wawira Gichoya and Imon Banerjee},
doi = {10.1038/s41746-021-00461-0},
year = {2021},
date = {2021-06-03},
urldate = {2021-01-01},
journal = {NPJ digital medicine},
volume = {4},
number = {1},
pages = {1--9},
publisher = {Nature Publishing Group},
abstract = {The strain on healthcare resources brought forth by the recent COVID-19 pandemic has highlighted the need for efficient resource planning and allocation through the prediction of future consumption. Machine learning can predict resource utilization such as the need for hospitalization based on past medical data stored in electronic medical records (EMR). We conducted this study on 3194 patients (46% male with mean age 56.7 (±16.8), 56% African American, 7% Hispanic) flagged as COVID-19 positive cases in 12 centers under Emory Healthcare network from February 2020 to September 2020, to assess whether a COVID-19 positive patient’s need for hospitalization can be predicted at the time of RT-PCR test using the EMR data prior to the test. Five main modalities of EMR, i.e., demographics, medication, past medical procedures, comorbidities, and laboratory results, were used as features for predictive modeling, both individually and fused together using late, middle, and early fusion. Models were evaluated in terms of precision, recall, F1-score (within 95% confidence interval). The early fusion model is the most effective predictor with 84% overall F1-score [CI 82.1\textendash86.1]. The predictive performance of the model drops by 6 % when using recent clinical data while omitting the long-term medical history. Feature importance analysis indicates that history of cardiovascular disease, emergency room visits in the past year prior to testing, and demographic factors are predictive of the disease trajectory. We conclude that fusion modeling using medical history and current treatment data can forecast the need for hospitalization for patients infected with COVID-19 at the time of the RT-PCR test.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Goyal, Shreya; Oluwalade, Bolu; Phillips, Tyler; Wu, Huanmei; Zou, Xukai
Usability and Security of Different Authentication Methods for an Electronic Health Records System Conference
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF), 2021.
@conference{purkayastha2021usability,
title = {Usability and Security of Different Authentication Methods for an Electronic Health Records System},
author = {Saptarshi Purkayastha and Shreya Goyal and Bolu Oluwalade and Tyler Phillips and Huanmei Wu and Xukai Zou},
year = {2021},
date = {2021-03-13},
booktitle = {Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (HEALTHINF)},
abstract = {We conducted a survey of 67 graduate students enrolled in the Privacy and Security in Healthcare course at Indiana University Purdue University Indianapolis. This was done to measure user preference and their understanding of usability and security of three different Electronic Health Records authentication methods: single authentication method (username and password), Single sign-on with Central Authentication Service (CAS) authentication method, and a bio-capsule facial authentication method. This research aims to explore the relationship between security and usability, and measure the effect of perceived security on usability in these three aforementioned authentication methods. We developed a formative-formative Partial Least Square Structural Equation Modeling (PLS-SEM) model to measure the relationship between the latent variables of Usability, and Security. The measurement model was developed using five observed variables (measures).-Efficiency and Effectiveness, Satisfaction, Preference, Concerns, and Confidence. The results obtained highlight the importance and impact of these measures on the latent variables and the relationship among the latent variables. From the PLS-SEM analysis, it was found that security has a positive impact on usability for Single sign-on and bio-capsule facial authentication methods. We conclude that the facial authentication method was the most secure and usable among the three authentication methods. Further, descriptive analysis was done to draw out the interesting findings from the survey regarding the observed variables.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Umoren, Rachel A; Patel, Shruti; Bucher, Sherri L; Esamai, Fabian; Ezeaka, Chinyere; Muinga, Naomi; Edgcombe, Hilary; Ezenwa, Beatrice; Fajolu, Iretiola; Feltner, John; others,
2021.
@misc{umoren2021attitudes,
title = {Attitudes Of Healthcare Workers In Low-Resource Settings To Mobile Virtual Reality Simulations For Newborn Resuscitation Training--A Report From The eHBB/mHBS Study},
author = {Rachel A Umoren and Shruti Patel and Sherri L Bucher and Fabian Esamai and Chinyere Ezeaka and Naomi Muinga and Hilary Edgcombe and Beatrice Ezenwa and Iretiola Fajolu and John Feltner and others},
doi = {10.1542/peds.147.3MA3.229},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
publisher = {Am Acad Pediatrics},
abstract = {Background: Virtual simulations provide opportunities for interactive learning, problem-solving, and standardized feedback. Little is known about the attitudes of healthcare workers to using mobile virtual reality (VR) simulations for newborn resuscitation training. Objective: To describe the perceptions and attitudes of healthcare workers in low resource settings towards using mobile VR simulations for neonatal resuscitation training. Methods: From July 2018 to September 2019, nine focus group discussions (FGD) with 5-8 participants per group were held with healthcare workers enrolled in the eHBB/mHBS study on using mobile VR simulations for nedwborn resuscitation training in Nigeria and Kenya. The focus group facilitators used a semi-structured interview guide designed to elicit participants’ experiences with and opinions about using mobile VR for healthcare education in a low resource setting. FGD were audio-recorded and transcribed for qualitative analysis. Data were organized using NVIVO 12 software [QSI International] was used to organize the data...},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Purkayastha, Saptarshi; Trivedi, Hari; Gichoya, Judy Wawira
Failures hiding in success for artificial intelligence in radiology Journal Article
In: Journal of the American College of Radiology, vol. 18, no. 3, pp. 517–519, 2021.
@article{purkayastha2021failuresb,
title = {Failures hiding in success for artificial intelligence in radiology},
author = {Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya},
doi = {10.1016/j.jacr.2020.11.008},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
journal = {Journal of the American College of Radiology},
volume = {18},
number = {3},
pages = {517--519},
publisher = {Elsevier},
abstract = {Reports of computer algorithms outperforming radiologists have persisted over the last 15 years, starting with the 2005 publication by Rubin et al on detecting pulmonary nodules from CT scans [ 1
]. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
]. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently.
Umoren, Rachel A; Esamai, Fabian; Ezeaka, Chinyere; Ezenwa, Beatrice; Fajolu, Iretiola; Nafula, Mary; Makokha, Felicitas; Hippe, Dan; Feltner, John; Patel, Shruti; others,
2021.
@misc{umoren2021ehbb,
title = {eHBB: A Randomized Controlled Trial Of Virtual Reality For Newborn Resuscitation Refresher Training Of Healthcare Workers In Nigeria And Kenya},
author = {Rachel A Umoren and Fabian Esamai and Chinyere Ezeaka and Beatrice Ezenwa and Iretiola Fajolu and Mary Nafula and Felicitas Makokha and Dan Hippe and John Feltner and Shruti Patel and others},
doi = {10.1542/peds.147.3MA3.237},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
publisher = {Am Acad Pediatrics},
abstract = {Background: Each year, there are 2.8 million newborn deaths, most of which are preventable. Intrapartum asphyxia is one of the three leading causes of neonatal mortality. In 2017, poor quality of care accounted for almost 1 million neonatal deaths, mostly during the intrapartum period. As the majority of these deaths occur in low- and middle-income country settings where there is high penetrance of mobile devices, we hypothesized that mobile virtual reality (VR) simulation refresher training in neonatal resuscitation (NR) would support the maintenance of HCW NR skills over time. Methods: Healthcare workers who work in labor and delivery and newborn care units at secondary and tertiary healthcare facilities in Lagos, Nigeria and Busia, Western Kenya and who had not received training in Helping Babies Breathe in the past one year were recruited to participate in the study. Participants were consented and randomized to receive the eHBB VR + digital manual,...},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Bucher, Sherri L; Rajapuri, Anushri; Ravindran, Radhika; Rukunga, Janet; Horan, Kevin; Esamai, Fabian; Purkayastha, Saptarshi
2021.
@misc{bucher2021essential,
title = {The Essential Care For Every Baby Digital Action Plan: Design And Usability Testing Of A Mobile Phone-Based Newborn Care Decision Support Tool In Kenya},
author = {Sherri L Bucher and Anushri Rajapuri and Radhika Ravindran and Janet Rukunga and Kevin Horan and Fabian Esamai and Saptarshi Purkayastha},
doi = {10.1542/peds.147.3MA3.263},
year = {2021},
date = {2021-03-01},
urldate = {2021-01-01},
publisher = {American Academy of Pediatrics},
abstract = {Background: Each year, there are 2.5 million neonatal deaths, primarily within low/middle-income countries (LMICs). Helping Babies Survive educational and training programs, including Essential Care for Every Baby (ECEB), equip LMIC healthcare providers (HCPs) with the knowledge, skills, and competencies to save newborn lives. Concurrently, growing access to mobile phones and improved network connectivity in LM4ICs open the possibility for digital decision support tools to assist HCPs to provide higher quality newborn care. Our team has developed an integrated suite of apps, mobile Helping Babies Survive powered by DHIS2 (mHBS/DHIS2), which are purpose-built to support effective implementation of Helping Babies Survive initiatives around the world. Existing mHBS/DHIS2 functionality includes: education, training, monitoring and evaluation, and quality improvement for Helping Babies Breathe. Here, we describe expanding mHBS/DHIS2 capabilities to include decision support for ECEB. Purpose: To describe the design and evaluation of a novel mobile phone-based digital decision-support tool for essential newborn...},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Purkayastha, Saptarshi; Trivedi, Hari; Gichoya, Judy Wawira
Failures hiding in success for artificial intelligence in radiology Journal Article
In: Journal of the American College of Radiology, vol. 18, no. 3, pp. 517-519, 2021.
@article{purkayastha2021failures,
title = {Failures hiding in success for artificial intelligence in radiology},
author = {Saptarshi Purkayastha and Hari Trivedi and Judy Wawira Gichoya},
doi = {10.1016/j.jacr.2020.11.008},
year = {2021},
date = {2021-03-01},
journal = {Journal of the American College of Radiology},
volume = {18},
number = {3},
pages = {517-519},
abstract = {Reports of computer algorithms outperforming radiologists have persisted over the last 15 years, starting with the 2005 publication by Rubin et al on detecting pulmonary nodules from CT scans. Back then, these technologies were referred to as computer-aided diagnosis, which could be considered as a precursor, of sorts, to what is now referred to broadly as artificial intelligence (AI). Technology gains in hardware over the past 5 years have facilitated the training of deep neural networks with millions of parameters, exponentially accelerating the pace of AI publications. However, like every other scientific field, the successes of AI in radiology are published and publicized with much fanfare, and failures are not discussed or made public. In fact, most AI failures are discovered anecdotally from personal experience or when shared in social media as tweets or blog posts. In this article, we discuss some pitfalls frequently encountered in reporting the success of AI in radiology, which might be considered failures when considered differently. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Oluwalade, Bolu; Neela, Sunil; Wawira, Judy; Adejumo, Tobiloba; Purkayastha, Saptarshi
Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data Conference
Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021), vol. 5, Science and Technology Publications, 2021, ISBN: 978-989-758-490-9.
@conference{oluwalade2021human,
title = {Human Activity Recognition using Deep Learning Models on Smartphones and Smartwatches Sensor Data},
author = {Bolu Oluwalade and Sunil Neela and Judy Wawira and Tobiloba Adejumo and Saptarshi Purkayastha},
doi = {10.5220/0010325906450650},
isbn = {978-989-758-490-9},
year = {2021},
date = {2021-02-11},
booktitle = {Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021)},
volume = {5},
pages = {645-650},
publisher = {Science and Technology Publications},
abstract = {In recent years, human activity recognition has garnered considerable attention both in industrial and academic research because of the wide deployment of sensors, such as accelerometers and gyroscopes, in products such as smartphones and smartwatches. Activity recognition is currently applied in various fields where valuable information about an individual’s functional ability and lifestyle is needed. In this study, we used the popular WISDM dataset for activity recognition. Using multivariate analysis of covariance (MANCOVA), we established a statistically significant difference (p\< 0.05) between the data generated from the sensors embedded in smartphones and smartwatches. By doing this, we show that smartphones and smartwatches don’t capture data in the same way due to the location where they are worn. We deployed several neural network architectures to classify 15 different hand and non-hand-oriented activities. These models include Long short-term memory (LSTM), Bi-directional Long short-term memory (BiLSTM), Convolutional Neural Network (CNN), and Convolutional LSTM (ConvLSTM). The developed models performed best with watch accelerometer data. Also, we saw that the classification precision obtained with the convolutional input classifiers (CNN and ConvLSTM) was higher than the end-to-end LSTM classifier in 12 of the 15 activities. Additionally, the CNN model for the watch accelerometer was better able to classify non-hand oriented activities when compared to hand-oriented activities.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Purkayastha, Saptarshi
Electronic Patient Records as a Substrate for Collaboration for Distributed Care in Low-Resource Contexts Proceedings Article
In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 289–295, IEEE 2021.
@inproceedings{purkayastha2021electronic,
title = {Electronic Patient Records as a Substrate for Collaboration for Distributed Care in Low-Resource Contexts},
author = {Saptarshi Purkayastha},
doi = {10.1109/ICHI52183.2021.00052},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)},
pages = {289--295},
organization = {IEEE},
abstract = {Collaboration to provide patient care in low-resource contexts has been a challenge due to heavy patient load, limited connectivity, and knowledge-gap between primary and tertiary care. Through the design, development, and implementation of a private social network-connected, large-scale hospital information system, which has scaled to several zonal and district hospitals in a small hilly country in South East Asia, we present the case study of a system that has enabled collaboration. Using coordination mechanisms as a theoretical framework, we discuss some methods of collaboration. In the paper, we present electronic patient records (EPR) as the substrate that enables collaboration between providers, departments, developers throughout the health systems. In our analysis, we present useful learnings of collaboration between provider-provider, developer-developer, provider-patient, implementer-provider, and how the balance of these is a necessary condition to create a useful substrate for collaboration.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2020
Tariq, Amara; Purkayastha, Saptarshi; Padmanaban, Geetha Priya; Krupinski, Elizabeth; Trivedi, Hari; Banerjee, Imon; Gichoya, Judy Wawira
Current clinical applications of artificial intelligence in radiology and their best supporting evidence Journal Article
In: Journal of the American College of Radiology, vol. 17, no. 11, pp. 1371-1381, 2020.
@article{tariq2020current,
title = {Current clinical applications of artificial intelligence in radiology and their best supporting evidence},
author = {Amara Tariq and Saptarshi Purkayastha and Geetha Priya Padmanaban and Elizabeth Krupinski and Hari Trivedi and Imon Banerjee and Judy Wawira Gichoya},
doi = {10.1016/j.jacr.2020.08.018},
year = {2020},
date = {2020-11-02},
urldate = {2020-11-02},
journal = {Journal of the American College of Radiology},
volume = {17},
number = {11},
pages = {1371-1381},
publisher = {Elsevier},
abstract = {Purpose
Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.
Methods
A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools.
Results
There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products.
Conclusions
Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Despite tremendous gains from deep learning and the promise of artificial intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large translational gap to implement and use AI products in real-world clinical situations. Adoption of standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, Consolidated Standards of Reporting Trials, and the Checklist for Artificial Intelligence in Medical Imaging is increasing to improve the peer-review process and reporting of AI tools. However, no such standards exist for product-level review.
Methods
A review of clinical trials showed a paucity of evidence for radiology AI products; thus, the authors developed a 10-question assessment tool for reviewing AI products with an emphasis on their validation and result dissemination. The assessment tool was applied to commercial and open-source algorithms used for diagnosis to extract evidence on the clinical utility of the tools.
Results
There is limited technical information on methodologies for FDA-approved algorithms compared with open-source products, likely because of intellectual property concerns. Furthermore, FDA-approved products use much smaller data sets compared with open-source AI tools, because the terms of use of public data sets are limited to academic and noncommercial entities, which precludes their use in commercial products.
Conclusions
Overall, this study reveals a broad spectrum of maturity and clinical use of AI products, but a large gap exists in exploring the actual performance of AI tools in clinical practice.
Bucher, Sherri; Hoilett, Orlando; Bluhm, Nicholas; Walters, Benjamin; Pickering, Alyson; Purkayastha, Saptarshi; Linnes, Jacqueline C; Esamai, Fabian; Ummel, Jason; Ekhaguere, Osayame
Wireless vital signs monitoring of opioid-exposed newborns during skin-to-skin care: Biomedical device innovation Presentation
28.10.2020.
@misc{bucher2020wireless,
title = {Wireless vital signs monitoring of opioid-exposed newborns during skin-to-skin care: Biomedical device innovation},
author = {Sherri Bucher and Orlando Hoilett and Nicholas Bluhm and Benjamin Walters and Alyson Pickering and Saptarshi Purkayastha and Jacqueline C Linnes and Fabian Esamai and Jason Ummel and Osayame Ekhaguere},
url = {https://apha.confex.com/apha/2020/meetingapp.cgi/Paper/477733},
year = {2020},
date = {2020-10-28},
booktitle = {APHA's 2020 VIRTUAL Annual Meeting and Expo (Oct. 24-28)},
publisher = {American Public Health Association},
abstract = {Background/Significance: Indiana has one of the highest rates of infant mortality in the United States. Premature birth, low birthweight, and in utero exposure to drugs of abuse are leading causes of neonatal complications. In 2016, 22% of umbilical cord blood samples from Indiana newborns tested positive for exposure to opiates. Health care providers are increasingly utilizing non-pharmacological strategies to manage the symptoms of opioid-exposed babies with neonatal abstinence syndrome (NAS). NAS babies require frequent vital signs monitoring, which can be a barrier to prolonged skin-to-skin care (STS).
Methods: Our multidisciplinary, cross-institutional team utilized human-centered and participatory design, agile development, qualitative (focus group discussions; key informant interviews), and quantitative assessments to design, develop, build, and evaluate an integrated biomedical device/digital health solution.
Results: Our 3-part, bundled innovation includes: (1) Built prototype of a wearable biomedical device with two integrated components: a carrier (worn by an adult caregiver) and a “pouch” (worn by the infant); (2) Wireless sensor technology which automatically, continuously, and accurately monitors key infant vital signs including body temperature, breathing, and heart rate, across three device use modes, including during STS care among adult caregiver-newborn dyads; (3) Android app which collects and displays device and infant vital signs monitoring information, and provides a platform for digitized educational resources. Feasibility assessments indicate broad acceptability of the device and app among health care providers, parents, and family stakeholders. Engineering verification has confirmed that our wireless sensor technology for measuring temperature, heart and respiratory rate during STS compares to the gold standard (impedance pneumography).
Conclusion: To facilitate non-pharmacological management of opioid-exposed babies, we have developed a prototype of a wearable biomedical device with built-in sensor technology for continuous wireless vital signs monitoring. Infant vital signs information is collected, and displayed, on an Android app. This innovative device potentially equips health care providers with additional tools to support non-pharmacological management of opioid-exposed babies.},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Methods: Our multidisciplinary, cross-institutional team utilized human-centered and participatory design, agile development, qualitative (focus group discussions; key informant interviews), and quantitative assessments to design, develop, build, and evaluate an integrated biomedical device/digital health solution.
Results: Our 3-part, bundled innovation includes: (1) Built prototype of a wearable biomedical device with two integrated components: a carrier (worn by an adult caregiver) and a “pouch” (worn by the infant); (2) Wireless sensor technology which automatically, continuously, and accurately monitors key infant vital signs including body temperature, breathing, and heart rate, across three device use modes, including during STS care among adult caregiver-newborn dyads; (3) Android app which collects and displays device and infant vital signs monitoring information, and provides a platform for digitized educational resources. Feasibility assessments indicate broad acceptability of the device and app among health care providers, parents, and family stakeholders. Engineering verification has confirmed that our wireless sensor technology for measuring temperature, heart and respiratory rate during STS compares to the gold standard (impedance pneumography).
Conclusion: To facilitate non-pharmacological management of opioid-exposed babies, we have developed a prototype of a wearable biomedical device with built-in sensor technology for continuous wireless vital signs monitoring. Infant vital signs information is collected, and displayed, on an Android app. This innovative device potentially equips health care providers with additional tools to support non-pharmacological management of opioid-exposed babies.
Goyal, Shreya; Purkayastha, Saptarshi; Phillips, Tyler; Quick, Rob; Britt, Alexis
Enabling Secure and Effective Biomedical Data Sharing through Cyberinfrastructure Gateways Conference
Gateways 2020, 2020.
@conference{goyal2020enabling,
title = {Enabling Secure and Effective Biomedical Data Sharing through Cyberinfrastructure Gateways},
author = {Shreya Goyal and Saptarshi Purkayastha and Tyler Phillips and Rob Quick and Alexis Britt},
doi = {10.17605/OSF.IO/6Y8WG},
year = {2020},
date = {2020-10-19},
urldate = {2020-10-19},
booktitle = {Gateways 2020},
journal = {arXiv preprint arXiv:2012.12835},
abstract = {Dynaswap project reports on developing a coherently integrated and trustworthy holistic secure workflow protection architecture for cyberinfrastructures which can be used on virtual machines deployed through cyberinfrastructure (CI) services such as JetStream. This service creates a user-friendly cloud environment designed to give researchers access to interactive computing and data analysis resources on demand. The Dynaswap cybersecurity architecture supports roles, role hierarchies, and data hierarchies, as well as dynamic changes of roles and hierarchical relations within the scientific infrastructure. Dynaswap combines existing cutting-edge security frameworks (including an Authentication Authorization-Accounting framework, Multi-Factor Authentication, Secure Digital Provenance, and Blockchain) with advanced security tools (e.g., Biometric-Capsule, Cryptography-based Hierarchical Access Control, and Dual-level Key Management). The CI is being validated in life-science research environments and in the education settings of Health Informatics. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Bucher, Sherri L.; Cardellichio, Peter; Muinga, Naomi; Patterson, Jackie K.; Thukral, Anu; Deorari, Ashok K.; Data, Santorino; Umoren, Rachel; Purkayastha, Saptarshi
Digital health innovations, tools, and resources to support Helping Babies Survive programs Journal Article
In: Pediatrics, vol. 146, no. Supplement 2, pp. S165-S182, 2020, ISSN: 0031-4005.
@article{bucher2020digital,
title = {Digital health innovations, tools, and resources to support Helping Babies Survive programs},
author = {Sherri L. Bucher and Peter Cardellichio and Naomi Muinga and Jackie K. Patterson and Anu Thukral and Ashok K. Deorari and Santorino Data and Rachel Umoren and Saptarshi Purkayastha},
url = {https://pediatrics.aappublications.org/content/146/Supplement_2/S165},
doi = {10.1542/peds.2020-016915I},
issn = {0031-4005},
year = {2020},
date = {2020-10-01},
journal = {Pediatrics},
volume = {146},
number = {Supplement 2},
pages = {S165-S182},
abstract = {The Helping Babies Survive (HBS) initiative features a suite of evidence-based curricula and simulation-based training programs designed to provide health workers in low- and middle-income countries (LMICs) with the knowledge, skills, and competencies to prevent, recognize, and manage leading causes of newborn morbidity and mortality. Global scale-up of HBS initiatives has been rapid. As HBS initiatives rolled out across LMIC settings, numerous bottlenecks, gaps, and barriers to the effective, consistent dissemination and implementation of the programs, across both the pre- and in-service continuums, emerged. Within the first decade of expansive scale-up of HBS programs, mobile phone ownership and access to cellular networks have also concomitantly surged in LMICs. In this article, we describe a number of HBS digital health innovations and resources that have been developed from 2010 to 2020 to support education and training, data collection for monitoring and evaluation, clinical decision support, and quality improvement. Helping Babies Survive partners and stakeholders can potentially integrate the described digital tools with HBS dissemination and implementation efforts in a myriad of ways to support low-dose high-frequency skills practice, in-person refresher courses, continuing medical and nursing education, on-the-job training, or peer-to-peer learning, and strengthen data collection for key newborn care and quality improvement indicators and outcomes. Thoughtful integration of purpose-built digital health tools, innovations, and resources may assist HBS practitioners to more effectively disseminate and implement newborn care programs in LMICs, and facilitate progress toward the achievement of Sustainable Development Goal health goals, targets, and objectives.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Bhimireddy, Ananth; Sinha, Priyanshu; Oluwalade, Bolu; Gichoya, Judy Wawira; Purkayastha, Saptarshi
Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks Proceedings Article
In: Bach, Kerstin; Bunescu, Razvan; Marling, Cindy; Wiratunga, Nirmalie (Ed.): Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), pp. 125-130, CEUR-WS Workshop Proceedings, 2020.
@inproceedings{bhimireddy2020blood,
title = {Blood Glucose Level Prediction as Time-Series Modeling using Sequence-to-Sequence Neural Networks},
author = {Ananth Bhimireddy and Priyanshu Sinha and Bolu Oluwalade and Judy Wawira Gichoya and Saptarshi Purkayastha},
editor = {Kerstin Bach and Razvan Bunescu and Cindy Marling and Nirmalie Wiratunga},
url = {http://ceur-ws.org/Vol-2675/paper22.pdf},
year = {2020},
date = {2020-09-17},
booktitle = {Proceedings of the 5th International Workshop on Knowledge Discovery in Healthcare Data
co-located with 24th European Conference on Artificial Intelligence (ECAI 2020)},
volume = {Vol-2675},
pages = {125-130},
publisher = {CEUR-WS Workshop Proceedings},
abstract = {The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor blood glucose and administer insulin. This technology allows subjects to easily track their blood glucose levels with early intervention, preventing the need for hospital visits. The data collected from these sensors is an excellent candidate for the application of machine learning algorithms to learn patterns and predict future values of blood glucose levels. In this study, we developed artificial neural network algorithms based on the OhioT1DM training dataset that contains data on 12 subjects. The dataset contains features such as subject identifiers, continuous glucose monitoring data obtained in 5 minutes intervals, insulin infusion rate, etc. We developed individual models, including LSTM, BiLSTM, Convolutional LSTMs, TCN, and sequence-to-sequence models. We also developed transfer learning models based on the most important features of the data, as identified by a gradient boosting algorithm. These models were evaluated on the OhioT1DM test dataset that contains 6 unique subjects data. The model with the lowest RMSE values for the 30- and 60-minutes was selected as the best performing model. Our result shows that sequence-to-sequence BiLSTM performed better than the other models. This work demonstrates the potential of artificial neural networks algorithms in the management of Type 1 diabetes.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Rajapuri, Anushri S; Ravindran, Radhika; Horan, Kevin; Bucher, Sherri; Purkayastha, Saptarshi
Essential Care for Every Baby: Neonatal Clinical Decision Support Tool Conference
International Conference on Applied Human Factors and Ergonomics, vol. 1205, 2020, ISBN: 978-3-030-50837-1.
@conference{rajapuri2020essential,
title = {Essential Care for Every Baby: Neonatal Clinical Decision Support Tool},
author = {Anushri S Rajapuri and Radhika Ravindran and Kevin Horan and Sherri Bucher and Saptarshi Purkayastha},
editor = {Kalra J., Lightner N},
doi = {https://doi.org/10.1007/978-3-030-50838-8_26},
isbn = {978-3-030-50837-1},
year = {2020},
date = {2020-07-16},
booktitle = {International Conference on Applied Human Factors and Ergonomics},
volume = {1205},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}