2023
Purkayastha, Saptarshi; Merine, Regina; Dsouza, Vyona; Singh, Pallavi; Gichoya, Judy
Evaluating user acceptance of an open-source mobile app for hospital price transparency rule Journal Article
In: 2023.
@article{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 = {2023-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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}
}
Khairat, Saif; Feldman, Sue S; Rana, Arif; Faysel, Mohammad; Purkayastha, Saptarshi; Scotch, Matthew; Eldredge, Christina
Foundational domains and competencies for baccalaureate health informatics education Journal Article
In: Journal of the American Medical Informatics Association, pp. ocad147, 2023.
@article{khairat2023foundational,
title = {Foundational domains and competencies for baccalaureate health informatics education},
author = {Saif Khairat and Sue S Feldman and Arif Rana and Mohammad Faysel and Saptarshi Purkayastha and Matthew Scotch and Christina Eldredge},
year = {2023},
date = {2023-01-01},
journal = {Journal of the American Medical Informatics Association},
pages = {ocad147},
publisher = {Oxford University Press},
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; others,
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 others},
year = {2023},
date = {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},
year = {2023},
date = {2023-01-01},
journal = {Journal of the American College of Radiology},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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 = {2023-01-01},
journal = {Applied Sciences},
volume = {13},
number = {14},
pages = {8427},
publisher = {MDPI},
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},
booktitle = {epiDAMIK 6.0: The 6th International workshop on Epidemiology meets Data Mining and Knowledge Discovery at KDD 2023},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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},
journal = {IEEE Journal of Biomedical and Health Informatics},
publisher = {IEEE},
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},
booktitle = {International Conference on Artificial Intelligence in Medicine},
pages = {373\textendash377},
organization = {Springer Nature Switzerland Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2022
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},
booktitle = {Medical Imaging with Deep Learning},
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},
booktitle = {2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)},
pages = {67\textendash70},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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},
booktitle = {2022 IEEE Healthcare Innovations and Point of Care Technologies (HI-POCT)},
pages = {26\textendash29},
organization = {IEEE},
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},
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},
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},
journal = {The Lancet Digital Health},
volume = {4},
number = {6},
pages = {e406\textendashe414},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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},
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},
publisher = {AIIM-D-22-00928},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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},
booktitle = {Workshop on Medical Image Learning with Limited and Noisy Data},
pages = {187\textendash196},
organization = {Springer Nature Switzerland Cham},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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},
booktitle = {2022 Ninth International Conference on Software Defined Systems (SDS)},
pages = {1\textendash8},
organization = {IEEE},
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}
}
Mathur, Varoon; Purkayashtha, Saptarshi; Gichoya, Judy Wawira
Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care Journal Article
In: arXiv preprint arXiv:2005.12378, 2020.
@article{mathur2020artificial,
title = {Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care},
author = {Varoon Mathur and Saptarshi Purkayashtha and Judy Wawira Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2005.12378},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Goyal, Shreya; Zou, Xukai; Purkayastha, Saptarshi; Wu, Huanmei
AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization Proceedings Article
In: 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 189–198, IEEE 2020.
@inproceedings{phillips2020authn,
title = {AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization},
author = {Tyler Phillips and Xiaoyuan Yu and Brandon Haakenson and Shreya Goyal and Xukai Zou and Saptarshi Purkayastha and Huanmei Wu},
year = {2020},
date = {2020-01-01},
booktitle = {2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)},
pages = {189--198},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Banerjee, Imon; Sinha, Priyanshu; Purkayastha, Saptarshi; Mashhaditafreshi, Nazanin; Tariq, Amara; Jeong, Jiwoong; Trivedi, Hari; Gichoya, Judy W
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications Journal Article
In: arXiv preprint arXiv:2006.13262, 2020.
@article{banerjee2020there,
title = {Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications},
author = {Imon Banerjee and Priyanshu Sinha and Saptarshi Purkayastha and Nazanin Mashhaditafreshi and Amara Tariq and Jiwoong Jeong and Hari Trivedi and Judy W Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2006.13262},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Umoren, Rachel A; Ezeaka, Chinyere; Esamai, Fabian; Kshatriya, Bhavani Agnikula; Avanigadda, Prem; Clopp, Bailey; Ezenwa, Beatrice; Fajolu, Iretiola; Feltner, John; Makokha, Felicitas; others,
Pre-Training Cognitive and Psychomotor Gaps in Healthcare Worker Neonatal Resuscitation Skills for Helping Babies Breathe--A Report from the eHBB/mHBS Study Miscellaneous
2020.
@misc{umoren2020pre,
title = {Pre-Training Cognitive and Psychomotor Gaps in Healthcare Worker Neonatal Resuscitation Skills for Helping Babies Breathe--A Report from the eHBB/mHBS Study},
author = {Rachel A Umoren and Chinyere Ezeaka and Fabian Esamai and Bhavani Agnikula Kshatriya and Prem Avanigadda and Bailey Clopp and Beatrice Ezenwa and Iretiola Fajolu and John Feltner and Felicitas Makokha and others},
year = {2020},
date = {2020-01-01},
publisher = {American Academy of Pediatrics},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Umoren, Rachel A; Bucher, Sherri; Purkayastha, Saptarshi; Ezeaka, Chinyere; Esamai, Fabian; Mairami, Amsa; Asangansi, Ime; Bresnahan, Brian; Paton, Chris
eHBB/mHBS-DHIS2: Mobile Virtual Reality Provider Training in Helping Babies Breathetextregistered Miscellaneous
2020.
@misc{umoren2020ehbb,
title = {eHBB/mHBS-DHIS2: Mobile Virtual Reality Provider Training in Helping Babies Breathetextregistered},
author = {Rachel A Umoren and Sherri Bucher and Saptarshi Purkayastha and Chinyere Ezeaka and Fabian Esamai and Amsa Mairami and Ime Asangansi and Brian Bresnahan and Chris Paton},
year = {2020},
date = {2020-01-01},
publisher = {American Academy of Pediatrics},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}