Unknown

Dataset Information

0

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 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.

Conclusions

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

SUBMITTER: Vaid A 

PROVIDER: S-EPMC7842859 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

altmetric image

Publications


<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Meth  ...[more]

Similar Datasets

| S-EPMC7430624 | biostudies-literature
| S-EPMC7641783 | biostudies-literature
| S-EPMC7693189 | biostudies-literature
| S-EPMC8407576 | biostudies-literature
| S-EPMC6857973 | biostudies-literature
| S-EPMC7556423 | biostudies-literature
| S-EPMC8293479 | biostudies-literature
| S-EPMC8185616 | biostudies-literature
| S-EPMC6380543 | biostudies-literature
| S-EPMC8413709 | biostudies-literature