The Hidden Patient Connections: Predicting Hormonal Therapy Medication Discontinuation Using Hypergraph Neural Network on Clinical Communications.

Abstract

Hormonal therapy is an important adjuvant treatment for breast cancer patients, but medication discontinuation of such therapy is not uncommon. The goal of this paper is to conduct research on the modeling of clinic communications, which have shown value in understanding medication discontinuation, to predict the discontinuation of hormonal therapy medications. Notably, we leveraged the Hypergraph Neural Network to capture the hidden connections of patients that were inferred from clinical communications. Combining the content of clinical communications as well as the demographics, insurance, and cancer stage information, our model achieved an AUC of 67.9%, which significantly outperformed other baselines such as Graph Convolutional Network (65.3%), Random Forest (62.7%), and Support Vector Machine (62.8%). Our study suggested that incorporating the hidden patient connections encoded in clinical communications into prediction models could boost their performance. Future research would consider combining structured medical records and clinical communications to better predict medication discontinuation.