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Vaid A, Jaladanki SK, Xu J, Teng S, Kumar A, Lee S, Somani S, Paranjpe I, De Freitas JK, Wanyan T, Johnson KW, Bicak M, Klang E, Kwon YJ, Costa A, Zhao S, Miotto R, Charney AW, Böttinger E, Fayad ZA, Nadkarni GN, Wang F, Glicksberg BS
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach
Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19
Akhil Vaid;
Suraj K Jaladanki;
Jie Xu;
Shelly Teng;
Arvind Kumar;
Samuel Lee;
Sulaiman Somani;
Ishan Paranjpe;
Jessica K De Freitas;
Tingyi Wanyan;
Kipp W Johnson;
Mesude Bicak;
Eyal Klang;
Young Joon Kwon;
Anthony Costa;
Shan Zhao;
Riccardo Miotto;
Alexander W Charney;
Erwin Böttinger;
Zahi A Fayad;
Girish N Nadkarni;
Fei Wang;
Benjamin S Glicksberg
ABSTRACT
Background:
Machine learning (ML) models require large datasets which may be siloed across different healthcare institutions. Current ML studies focusing on coronavirus disease 2019 (COVID-19) are limited to single hospital data which limits model generalizability.
Objective:
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.
Methods:
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.
Results:
LASSO-federated outperformed LASSO-local at three hospitals, and MLP-federated performed better than MLP-local at all five hospitals as measured by area under the receiver-operating characteristic (AUC-ROC). LASSO-pooled outperformed LASSO-federated at all hospitals, and MLP-federated outperformed MLP-pooled at two hospitals. Average model performance across all five hospitals was 0.666 (95% CI: 0.662-0.671) for LASSO-local, 0.792 (95% CI: 0.790-0.794) for LASSO-pooled, 0.766 (95% CI: 0.763-0.768) for LASSO-federated, 0.766 (95% CI: 0.763-0.769) for MLP-local, 0.798 (95% CI: 0.796-0.800) for MLP-pooled, and 0.810 (95% CI: 0.808-0.812) for MLP-federated.
Conclusions:
Federated learning shows promise in COVID-19 EHR data to develop robust predictive models without compromising patient privacy.
Citation
Please cite as:
Vaid A, Jaladanki SK, Xu J, Teng S, Kumar A, Lee S, Somani S, Paranjpe I, De Freitas JK, Wanyan T, Johnson KW, Bicak M, Klang E, Kwon YJ, Costa A, Zhao S, Miotto R, Charney AW, Böttinger E, Fayad ZA, Nadkarni GN, Wang F, Glicksberg BS
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach