FEDERATED LEARNING OF ROBUST INDIVIDUALIZED DECISION RULES WITH APPLICATION TO HETEROGENEOUS MULTIHOSPITAL SEPSIS POPULATION.

Abstract

Sepsis is a life-threatening condition affecting millions of individuals in the U.S. each year. The complexity of sepsis clinical management makes individualized treatment approaches desirable. The University of Pittsburgh Medical Center (UPMC) has collected electronic health records data of sepsis patients from multiple hospitals. The goal of this study is to derive individualized decision rules (IDRs) that could be safely applied to and uniformly improve decision-making across hospitals in the UPMC Health System by only using a subset of hospitals for training. Traditional approaches assume that data are sampled from a single population of interest. With multiple hospitals that vary in patient populations, treatments, and provider teams, an IDR that is successful in one hospital may not be as effective in another, and the performance achieved by a globally optimal IDR may vary greatly across hospitals, preventing it from being safely applied to unseen hospitals. To address these challenges as well as the practical restriction of data sharing across hospitals, we introduce a new objective function and a federated learning algorithm for learning IDRs that are robust to distributional uncertainty from heterogeneous data. The proposed framework uses a conditional maximin objective to enhance individual outcomes across hospitals, ensuring robustness against hospital-level variations. Compared to the traditional approach, the proposed method enhances the survival rate by 10 percentage points among patients who may experience extreme adverse outcomes across hospitals. Additionally, it increases the overall survival rate by two to three percentage points when the learned IDR is applied to unseen hospital populations.