Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

Link: https://doi.org/10.2196/24207
Authors: Vaid, Akhil; Jaladanki, Suraj K; Xu, Jie; Teng, Shelly; Kumar, Arvind; Lee, Samuel; Somani, Sulaiman; Paranjpe, Ishan; De Freitas, Jessica K; Wanyan, Tingyi; Johnson, Kipp W; Bicak, Mesude; Klang, Eyal; Kwon, Young Joon; Costa, Anthony; Zhao, Shan; Miotto, Riccardo; Charney, Alexander W; Böttinger, Erwin; Fayad, Zahi A; Nadkarni, Girish N; Wang, Fei; Glicksberg, Benjamin S

Abstract: Machine learning models require large datasets that may be siloed across different health care institutions. Machine learning studies that focus on COVID-19 have been limited to single-hospital data, which limits model generalizability. We aimed to use federated learning, a machine learning technique that avoids locally aggregating raw clinical data across multiple institutions, to predict mortality in hospitalized patients with COVID-19 within 7 days. Patient data were collected from the electronic health records of 5 hospitals within the Mount Sinai Health System. Logistic regression with L1 regularization/least absolute shrinkage and selection operator (LASSO) and multilayer perceptron (MLP) models were trained by using local data at each site. We developed a pooled model with combined data from all 5 sites, and a federated model that only shared parameters with a central aggregator. The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.