98 citations · 129 across the 9 of their papers we have counts for
9 papers
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Zhuohang Li, Chao Yan, Xinmeng Zhang +4
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generali…
A Multifaceted Benchmarking of Synthetic Electronic Health Record Generation Models
Chao Yan, Yao Yan, Zhiyu Wan +5
Synthetic health data have the potential to mitigate privacy concerns when sharing data to support biomedical research and the development of innovative healthcare applications. Mo…
Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database
Steve Nyemba, Chao Yan, Ziqi Zhang +4
Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision s…
Dynamically Adjusting Case Reporting Policy to Maximize Privacy and Utility in the Face of a Pandemic
J. Thomas Brown, Chao Yan, Weiyi Xia +5
Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, s…
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Prithwish Chakraborty, James Codella, Piyush Madan +22
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability…
Generating Electronic Health Records with Multiple Data Types and Constraints
Chao Yan, Ziqi Zhang, Steve Nyemba +1
Sharing electronic health records (EHRs) on a large scale may lead to privacy intrusions. Recent research has shown that risks may be mitigated by simulating EHRs through generativ…