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Adverse Drug Reaction (ADR) is a significant public health concernworld-wide. Numerous graph-based methods have been applied to biomedical graphsfor predicting ADRs in pre-marketing phases. ADR detection in post-marketsurveillance is no less important than pre-marketing assessment, and ADRdetection with large-scale clinical data have attracted much attention inrecent years. However, there are not many studies considering graph structuresfrom clinical data for detecting an ADR signal, which is a pair of aprescription and a diagnosis that might be a potential ADR. In this study, wedevelop a novel graph-based framework for ADR signal detection using healthcareclaims data. We construct a Drug-disease graph with nodes representing themedical 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 improvedAUROC and AUPRC performance of 0.795 and 0.775, compared to other algorithms,showing that it successfully learns node representations expressive of thoserelationships. Furthermore, our model predicts ADR pairs that do not exist inthe established ADR database, showing its capability to supplement the ADRdatabase.

Drug-disease Graph: Predicting Adverse Drug Reaction Signals via Graph Neural Network with Clinical Data