Generative Transformers for Pharmacovigilance Signal Detection using Electronic Health Records

Adverse drug reactions (ADRs) present substantial challenges to patient safety and to healthcare systems, often leading to hospitalizations and economic hardship. To mitigate these burdens, post-market surveillance is used to monitor ADRs after the conclusion of clinical trials. The commonly employed methods to estimate the strength of associations between drugs and ADRs from surveillance data include disproportionality metrics. However, these methods are limited in their ability to model temporal relationships indicating causality. Furthermore, they perform poorly when applied to comprehensive data from electronic health records (EHRs), failing to realize such data's potential to mitigate under-reporting and bias in ADR surveillance system data. To address these limitations, we propose novel methods using generative pre-trained transformers (GPT) for enhanced ADR signal detection in EHR data. On evaluations on data from two healthcare systems, the GPT models improved overall AUROC representing an absolute gain of 6-16 % over established baseline methods. This study highlights the potential of transformer-based models to advance pharmacovigilance by integrating comprehensive clinical data.