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

Weishen Pan; Zhenxing Xu; Suraj Rajendran; Fei Wang

doi:10.1016/j.patter.2023.100898

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

Patterns (N Y). 2023 Dec 26;5(1):100898. doi: 10.1016/j.patter.2023.100898. eCollection 2024 Jan 12.

Authors

Weishen Pan^{1

2}, Zhenxing Xu^{1

2}, Suraj Rajendran³, Fei Wang^{1

2}

Affiliations

¹ Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA.
² Institute of Artificial Intelligence for Digital Health, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA.
³ Tri-Institutional Computational Biology & Medicine Program, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA.

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.

Keywords: clinical risk predicion; distribution drift; federated learning.

Abstract

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