Elevance Health Inc.
Causal Recommender Engine for Chronic Disease Management

Abstract:

The present application describes a system and method for implementing a causal recommender for personalized disease treatment selection using machine learning. The method includes obtaining health trajectories for patients. Each health trajectory includes sub-trajectories. Each sub-trajectory includes a treatment event and ends at a respective index event. The method further includes stratifying the sub-trajectories for each patient to form stratified patient segments. Each segment corresponds to a separate and distinct health condition and includes the sub-trajectories for patients that have the health condition. For each segment, the method includes performing pairwise causal inference analysis on one or more treatments to estimate average treatment effect (ATE) values, and performing network meta-analysis on the ATE values, thereby ranking the one or more treatments. The method also includes reranking the one or more treatments after excluding unsafe treatments, and outputting treatment options based on ranked treatments for the segments.