Presented by Lu Tang, PhD, Assistant Professor and Vice Chair for Education, Department of Biostatistics, University of Pittsburgh

This paper introduces a new objective function and federated learning (FL) algorithm for deriving individualized decision rules (IDRs) that are robust to distributional uncertainty in heterogeneous data sources. It is motivated by the need to uniformly improve decision-making across multiple hospitals of a single health system. Traditional approaches assume that data are sampled from a single population of interest.  With multiple hospitals that vary in patient populations and treatments, an IDR that is effective in one hospital may not be as effective in another. Due to distributional heterogeneity, the performance achieved by a globally optimal IDR varies greatly across sites, preventing it from being safely applied to unseen sites. Furthermore, data from various hospitals cannot be pooled due to privacy concerns. To address these challenges, we developed an FL framework to learn IDRs from distributed data. The proposed framework introduces a conditional maximin objective to enhance individual outcomes across sites, ensuring robustness against site variations. The proposed method effectively improves the generalizability of IDRs for the management of sepsis in a hospital network.