Rare Disease Physician Targeting: A Factor Graph Approach

TL;DR

This paper introduces a factor graph approach for rare disease physician targeting, effectively handling data imbalance and providing interpretable relationship modeling to improve identification accuracy.

Contribution

It presents a novel graphical model method that jointly models physician and patient data, enhancing rare disease physician targeting beyond traditional segmentation techniques.

Findings

Improved accuracy in identifying target physicians

Enhanced interpretability through graph visualization

Effective handling of data imbalance in rare disease contexts

Abstract

In rare disease physician targeting, a major challenge is how to identify physicians who are treating diagnosed or underdiagnosed rare diseases patients. Rare diseases have extremely low incidence rate. For a specified rare disease, only a small number of patients are affected and a fractional of physicians are involved. The existing targeting methodologies, such as segmentation and profiling, are developed under mass market assumption. They are not suitable for rare disease market where the target classes are extremely imbalanced. The authors propose a graphical model approach to predict targets by jointly modeling physician and patient features from different data spaces and utilizing the extra relational information. Through an empirical example with medical claim and prescription data, the proposed approach demonstrates better accuracy in finding target physicians. The graph representation also provides visual interpretability of relationship among physicians and patients. The model can be extended to incorporate more complex dependency structures. This article contributes to the literature of exploring the benefit of utilizing relational dependencies among entities in healthcare industry.