  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.
