Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in children.

Our algorithm provides an effective distributed learning framework for disease subphenotyping using multi-site data based on aggregated data only. It facilitates high accuracy while properly accounts for the between-site heterogeneity. The results provide an update to the subphenotypes of MIS-C with larger and more recent data, aid in the understanding of the various disease patterns of MIS-C, and may improve the evaluation and intervention of MIS-C.