YiFan Wu

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 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.