Healthcare
We leverage human-machine collaborative learning and multimodal context-aware deep learning to advance healthcare quality and equity.
Funded Projects
- Advancing Health Equity using Interactive Condition Assessment and Monitoring – National Science Foundation (NSF) 2024
- Using human-centered AI for designing an intelligent telehealth training system – VT CHCI 2022
- Reducing Operating Room Waste by Monitoring Single-use Sterile Surgical Supplies with Computer Vision – NIH iTHRIV 2021
- Advancing Health Equity using Interactive Condition Assessment and Monitoring – National Science Foundation (NSF) 2021
- Determination of Safety Limits against Cyber Threats in Neuromodulation Devices using Machine Learning, Brain Phantoms, and Neural Pathways – Commonwealth Cyber Initiative (CCI) 2021
- Adaptive Artificial Intelligence to Model Consumer Behavior – Procter and Gamble 2017
- An Integrated Approach to Interactive Patient-Centric Monitoring – Xerox 2015
- Baby Activity Prediction for Effective Consumer Experience – Procter and Gamble 2014
- Baby Data Analytics: Towards Early Detection of Autism – PARC Internal Portfolio Investment Research Program 2014
Outreach
- Invited Talk
Machine Learning: Endless Possibilities
NIH iTHRIV Voices in Clinical Translational Science 2024 - Panelist
Healthcare Technology Governance
+Policy Symposium on Governance of New and Emerging Technologies 2023
Professional Service
- Advisory Board, LiteraSeed
Publications
2023
[c1] Prediction of Electric Fields Induced by Transcranial Magnetic Stimulation in the Brain using a Deep Encoder-Decoder Convolutional Neural Network
M. Tashli, M. Alam, J. Gong, C. Lewis, C. Peterson, H. Eldardiry, J. Atulasimha, and R. Hadimani
IEEE Internation EMBS Conference on Neural Engineering 2023
Acceptance Rate 24%
[c2] Prediction of Electric Fields Induced by Transcranial Magnetic Stimulation in the Brain using a Deep Encoder-Decoder Convolutional Neural Network
M. Tashli, M. Alam, J. Gong, C. Lewis, C. Peterson, H. Eldardiry, J. Atulasimha, R. Hadimani
Bulletin of the American Physical Society 2023
2022
[c3] Prediction of Stimulation Strength of Transcranial Magnetic Stimulation in the Brain with Deep Convolutional Neural Network based Encoder-Decoder Network
M. Alam, M. Tashli, J. Gong, C. Lewis , C. Peterson, H. Eldardiry, R. Hadimani, J. Atulasimha
IEEE Conference on Magnetism and Magnetic Materials 2022
2020
[p1] Interactive Remote Patient Monitoring and Condition Management Intervention System
H. Eldardiry, J. Rubin, R. Abreu, S. Ahern, D. Garcia, H. Du, A. Pattekar, and D. Bobrow
PATENT 2017049374 GRANTED 2020
2016
[c4] Time, Frequency & Complexity Analysis for Recognizing Panic States from Physiologic Time-Series
J. Rubin, R. Abreu, S. Ahern, H. Eldardiry, and D. Bobrow
ACM Pervasive Health Conference (PHC) 2016
2015
[c5] Towards a Mobile and Wearable System for Predicting Panic Attacks
J. Rubin, H. Eldardiry, R. Abreu, S. Ahern, D. Garcia, H. Du, and A. Pattekar
ACM International Conference on Pervasive and Ubiquitous Computing (UbiComp) 2015
Acceptance Rate 25.6%
[c6] A Wearable and Mobile Intervention Delivery System for Individuals with Panic Disorder
L. Cruz, J. Rubin, R. Abreu, S. Ahern, H. Eldardiry, and D. Bobrow
ACM International Conference on Mobile and Ubiquitous Multimedia (MUM) 2015
Acceptance Rate 37.1%
2014
[p2] Method and Apparatus for Combining Multidimensional Fraud Measurements for Anomaly Detection
Y. Zhang, J. Liu, and H. Eldardiry
PATENT 0244528 PUBLISHED 2014
2013
[c7] Fraud Detection for Healthcare
H. Eldardiry, J. Liu, Y. Zhang, and M. Fromherz
ACM Special Interest Group on Knowledge Discovery and Data Mining 1st Data Mining for Healthcare Workshop (SIGKDD-DMH) 2013
Acceptance Rate 17%
