Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes
This work addresses the problem of predicting personalized ADE risks for patients to help physicians choose safer treatments, but it is incremental as it builds on existing personalized prediction methods by incorporating medical history.
The study tackled personalized adverse drug event (ADE) risk prediction by developing a hierarchical time-aware neural network (HTNNR) that uses patient medical history from claims codes, and it substantially outperformed comparison methods, particularly for rare drugs.
Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of studies have been performed on detect correlation between a drug and an AE, limited studies have been conducted on personalized ADE risk prediction. Among treatment alternatives, avoiding the drug that has high likelihood of causing severe AE can help physicians to provide safer treatment to patients. Existing work on personalized ADE risk prediction uses the information obtained in the current medical visit. However, on the other hand, medical history reveals each patient's unique characteristics and comprehensive medical information. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship. The empirical evaluation show that the proposed HTNNR model substantially outperforms the comparison methods, especially for rare drugs.