Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals
Tsung-Ting Kuo, Rodney A. Gabriel, Jejo Koola, Robert T. Schooley, Lucila Ohno‐Machado
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 from Edinburgh, UK, D-CLEF performed close to the centralized solution, outperforming the siloed ones, and equivalent to the federated learning counterparts, but with increased synchronization time. Here, we show that D-CLEF presents a promising accelerator for healthcare systems to collaborate without submitting their patient data outside their own systems. Medical predictive analytics can enhance quality improvement, clinical research, and patient care. D-CLEF, a privacy-preserving model, integrates patient data across centers and performs comparably to centralized models, enabling collaboration without sharing patient records.