4.6 Article

Federated Learning for Electronic Health Records

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3514500

Keywords

Federated learning; electronic health records; healthcare; neural networks

Funding

  1. National Research Foundation, Singapore under its AI Singapore Programme [AISG-100E-2020-055, AISG-GC-2019-002A]
  2. NMRC [HSRG MOH-000030-00]

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Multi-center studies are preferred in data-driven medical research, but challenges in data sharing persist. Federated Learning offers a solution to data isolation.
In data-driven medical research, multi-center studies have long been preferred over single-center ones due to a single institute sometimes not having enough data to obtain sufficient statistical power for certain hypothesis testings as well as predictive and subgroup studies. The wide adoption of electronic health records (EHRs) has made multi-institutional collaboration much more feasible. However, concerns over infrastructures, regulations, privacy, and data standardization present a challenge to data sharing across healthcare institutions. Federated Learning (FL), which allows multiple sites to collaboratively train a global model without directly sharing data, has become a promising paradigm to break the data isolation. In this study, we surveyed existing works on FL applications in EHRs and evaluated the performance of current state-of-the-art FL algorithms on two EHR machine learning tasks of significant clinical importance on a real world multi-center EHR dataset.

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