Predicting rare events in markets with relational data

Abstract This study presents a modeling framework for predicting rare events in relational data settings. Focusing on the rare disease market, it introduces a factor graph model within a Bayesian classifier that jointly models physician and patient features through their complex visit relationships. The framework is applied to an empirical case focused on identifying physicians treating hereditary angioedema patients, using extensive prescription and medical claims data. Our analysis demonstrates the model’s effectiveness, showing it surpasses various benchmark models in identifying rare disease physicians, including those currently recognized in healthcare databases and those likely to emerge in the future. This research contributes to the existing literature by addressing the challenge of predicting rare disease physicians and highlighting the benefits of leveraging relational dependencies among distinct entities to forecast rare events.