An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.

TitleAn adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals.
Publication TypeJournal Article
Year of Publication2024
AuthorsPan W, Xu Z, Rajendran S, Wang F
JournalPatterns (N Y)
Volume5
Issue1
Pagination100898
Date Published2024 Jan 12
ISSN2666-3899
Abstract

Clinical risk prediction with electronic health records (EHR) using machine learning has attracted lots of attentions in recent years, where one of the key challenges is how to protect data privacy. Federated learning (FL) provides a promising framework for building predictive models by leveraging the data from multiple institutions without sharing them. However, data distribution drift across different institutions greatly impacts the performance of FL. In this paper, an adaptive FL framework was proposed to address this challenge. Our framework separated the input features into stable, domain-specific, and conditional-irrelevant parts according to their relationships to clinical outcomes. We evaluate this framework on the tasks of predicting the onset risk of sepsis and acute kidney injury (AKI) for patients in the intensive care unit (ICU) from multiple clinical institutions. The results showed that our framework can achieve better prediction performance compared with existing FL baselines and provide reasonable feature interpretations.

DOI10.1016/j.patter.2023.100898
Alternate JournalPatterns (N Y)
PubMed ID38264713
PubMed Central IDPMC10801228
Grant ListRF1 AG084178 / AG / NIA NIH HHS / United States
R01 AG080624 / AG / NIA NIH HHS / United States
T32 GM083937 / GM / NIGMS NIH HHS / United States
R01 AG080991 / AG / NIA NIH HHS / United States
RF1 AG072449 / AG / NIA NIH HHS / United States
R01 AG076234 / AG / NIA NIH HHS / United States
R01 MH124740 / MH / NIMH NIH HHS / United States
Division: 
Institute of Artificial Intelligence for Digital Health
Category: 
Faculty Publication