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

• Published in: JMIR medical informatics Vol. 9; no. 1; p. e24207
• Main 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
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 27.01.2021
• Subjects: Cardiovascular disease; Coronaviruses; COVID-19; Demographics; Electronic health records; Hospitals; Machine learning; Medical research; Mortality; Multimedia; Original Paper; Patients; COVID-19; electronic health records; machine learning; federated learning

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 techniqu...