Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

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

Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Using federated learning, a ML technique that avoids locally aggregating raw clinical data across multiple institutions, we predict mortality within seven days in hospitalized COVID-19 patients. Patient data was collected from Electronic Health Records (EHRs) from five hospitals within the Mount Sinai Health System (MSHS). Logistic Regression with L1 regularization (LASSO) and Multilayer Perceptron (MLP) models were trained using local data at each site, a pooled model with combined data from all five sites, and a federated model that only shared parameters with a central aggregator. Both the federated LASSO and federated MLP models performed better than their local model counterparts at four hospitals. The federated MLP model also outperformed the federated LASSO model at all hospitals. Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.