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

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
Source :
JMIR Medical Informatics, Vol 9, Iss 1, p e24207 (2021)
Publication Year :
2021
Publisher :
JMIR Publications, 2021.

Abstract

BackgroundMachine 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. ObjectiveWe 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. MethodsPatient 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. ResultsThe LASSOfederated model outperformed the LASSOlocal model at 3 hospitals, and the MLPfederated model performed better than the MLPlocal model at all 5 hospitals, as determined by the area under the receiver operating characteristic curve. The LASSOpooled model outperformed the LASSOfederated model at all hospitals, and the MLPfederated model outperformed the MLPpooled model at 2 hospitals. ConclusionsThe federated learning of COVID-19 electronic health record data shows promise in developing robust predictive models without compromising patient privacy.

Details

Language :
English
ISSN :
22919694
Volume :
9
Issue :
1
Database :
Directory of Open Access Journals
Journal :
JMIR Medical Informatics
Publication Type :
Academic Journal
Accession number :
edsdoj.11f1e0ca04304420af2f5d46261fc8ee
Document Type :
article
Full Text :
https://doi.org/10.2196/24207