Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes

TL;DR

This paper introduces HTNNR, a hierarchical time-aware neural network that leverages patient medical history from claim codes to predict personalized adverse drug event risks, outperforming existing methods especially for rare drugs.

Contribution

The study presents a novel HTNNR model that utilizes comprehensive medical history data for personalized ADE risk prediction, improving accuracy over prior approaches.

Findings

HTNNR significantly outperforms comparison methods.

The model is especially effective for rare drugs.

Incorporating medical history improves prediction accuracy.

Abstract

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.