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

Abstract

Background: 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.

Objective: 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.

Methods: 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.

Results: The LASSO_federated model outperformed the LASSO_local model at 3 hospitals, and the MLP_federated model performed better than the MLP_local model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSO_pooled model outperformed the LASSO_federated model at all hospitals, and the MLP_federated model outperformed the MLP_pooled model at 2 hospitals.

Conclusions: The federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Keywords: COVID-19; electronic health records; federated learning; machine learning.