  Adverse Drug Reaction (ADR) is a significant public health concern
world-wide. Numerous graph-based methods have been applied to biomedical graphs
for predicting ADRs in pre-marketing phases. ADR detection in post-market
surveillance is no less important than pre-marketing assessment, and ADR
detection with large-scale clinical data have attracted much attention in
recent years. However, there are not many studies considering graph structures
from clinical data for detecting an ADR signal, which is a pair of a
prescription and a diagnosis that might be a potential ADR. In this study, we
develop a novel graph-based framework for ADR signal detection using healthcare
claims data. We construct a Drug-disease graph with nodes representing the
medical codes. The edges are given as the relationships between two codes,
computed using the data. We apply Graph Neural Network to predict ADR signals,
using labels from the Side Effect Resource database. The model shows improved
AUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms,
showing that it successfully learns node representations expressive of those
relationships. Furthermore, our model predicts ADR pairs that do not exist in
the established ADR database, showing its capability to supplement the ADR
database.
