Predicting rare events in markets with relational data

Yong Cai (), Qiang Liu (), Yunlong Wang () and Fan Zhang ()
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Yong Cai: IQVIA, Plymouth Meeting
Qiang Liu: Purdue University, Mitch Daniels School of Business
Yunlong Wang: IQVIA, Plymouth Meeting
Fan Zhang: IQVIA, Plymouth Meeting

Quantitative Marketing and Economics (QME), 2025, vol. 23, issue 4, No 3, 545-588

Abstract: 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.

Keywords: Factor graph; Relational data; Healthcare; Bayesian network; Rare disease; Imbalanced classes; Rare events (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 C63 I11 L65 M31 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11129-025-09302-w

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