Leveraging temporality, dose effect, and co-medication to improve drug safety surveillance

Abstract

Adverse drug reactions (ADRs) rank among the top causes of morbidity and mortality worldwide, yet current post-market drug surveillance systems often relying on spontaneous reporting. They suffer from under-reporting of ADRs and limited capture of clinical context. This dissertation addresses these gaps by leveraging electronic health record (EHR) data and transformer-based models to detect ADRs and drug–drug interactions (DDIs) more effectively. First, we develop and evaluate a generative transformer architecture (GPT-2) trained from scratch on longitudinal EHR data from two distinct repositories (MIMIC-IV and a large university health system, UW). Unlike traditional disproportionality metrics that focus on cross-sectional drug-event co-occurrences, the proposed model captures temporal relationships and contextual dependencies among medications, diagnoses, and outcomes. Second, we introduce a "value-aware" embedding approach to incorporate continuous numeric data, such as drug dosages and lab measurements. Experimental results show that these value-aware embeddings further improve model performance, outperforming baseline transformer architectures that did not have numeric data. Third, we extend the model's scope to evaluate DDIs under polypharmacy conditions, demonstrating that a transformer exceeded the predictive accuracy of simpler machine learning baselines. Across all EHR datasets tested, the transformer-based methods consistently surpass existing standard approaches in ADR detection, measured by improved area under the receiver operating characteristic curve (AUROC). By capturing time-dependent patterns, integrating numeric variables, and accounting for interacting drug exposures, this work broadens the capabilities of pharmacovigilance beyond conventional "signal detection" practices. Despite practical limitations such as limited generalizability to other health systems and the need for rigorous validation—these findings demonstrated the promise of generative transformers as a scalable, data-driven framework for enhancing patient safety for pharmacovigilance.