Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.

ABSTRACT: Quality improvement, clinical research, and patient care can be supported by medical predictive analytics. Predictive models can be improved by integrating more patient records from different healthcare centers (horizontal) or integrating parts of information of a patient from different centers (vertical). We introduce Distributed Cross-Learning for Equitable Federated models (D-CLEF), which incorporates horizontally- or vertically-partitioned data without disseminating patient-level records, to protect patients' privacy. We compared D-CLEF with centralized/siloed/federated learning in horizontal or vertical scenarios. Using data of more than 15,000 patients with COVID-19 from five University of California (UC) Health medical centers, surgical data from UC San Diego, and heart disease data

SUBMITTER: Kuo TT 

PROVIDER: S-EPMC11799213 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature