Detecting Drug Safety Signals from National Taiwan Health Insurance Research Database: A Learning to Rank Approach

Pharmacovigilance (PhV) is a serious issue worldwide, because adverse drug effects are serious problems that cause harms to patients or even death. Traditionally, PhV research focuses on detecting adverse drug effects from spontaneous reports systems (SRS), which contains reports voluntarily reported by medical professionals, patients, and pharmaceutical companies. However, the volunteer nature of SRS databases causes some limitations (e.g., overreporting, data incompleteness). Thus, the PhV research starts to investigate the use of electronic health records (EHR) databases for drug safety signal detection in recent years. In this study, we propose a novel EHR-based drug safety signal detection method on the basis of the learning to rank approach. In addition to multiple disproportional analysis measures, our proposed method also incorporates as additional ranking variables that capture implicit relations between drugs and diseases for decreasing the importance of non-drug-outcome signals. We use Taiwan’s national health insurance research database for drug safety signal detection. Our evaluation results suggest that our proposed method significantly outperforms existing disproportional analysis methods (each of which uses a single disproportional analysis measures).