Prediction of antibiotic-associated cutaneous adverse drug reactions using electronic health record foundation models.

Cutaneous adverse drug reactions (CADRs) are the most common form of adverse drug reactions, ranging from mild rashes to life-threatening diseases, such as Stevens-Johnson syndrome and toxic epidermal necrolysis. However, there is no effective tool to predict antibiotic-associated CADRs. In this study, we propose an antibiotic-associated CADR prediction model using electronic health record (EHR) foundation models (FMs). EHR FMs are based on the pretraining-finetuning paradigms of language models, corresponding medical codes and their sequences to words and sentences. We included 802,131 inpatients across three tertiary hospitals in Korea, combining EHR data with nursing statements and reports to extract skin rash records. Our approach achieved the best predictive performance compared to all the other baseline models across all datasets. To enhance clinical relevance, we classified CADRs into immediate and delayed types and conducted a detailed sub-analysis. Finally, we found that properly configured EHR FMs can effectively predict the risk of developing antibiotics-associated CADRs, particularly for delayed-type reactions where predictive testing options are limited.