An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results

TL;DR

This paper introduces CT-ADE, a comprehensive dataset for predicting adverse drug events from clinical trial data, and evaluates large language models' performance in this task, highlighting the importance of contextual information.

Contribution

The paper presents CT-ADE, a novel dataset integrating treatment and population data for ADE prediction, and provides baseline results using large language models.

Findings

Best LLM achieved 56% F1-score in ADE prediction.

Models with treatment and patient info outperform structure-only models by 21%-38%.

CT-ADE enables comparative analysis across different treatment conditions.

Abstract

Adverse drug events (ADEs) are a major safety issue in clinical trials. Thus, predicting ADEs is key to developing safer medications and enhancing patient outcomes. To support this effort, we introduce CT-ADE, a dataset for multilabel ADE prediction in monopharmacy treatments. CT-ADE encompasses 2,497 drugs and 168,984 drug-ADE pairs from clinical trial results, annotated using the MedDRA ontology. Unlike existing resources, CT-ADE integrates treatment and target population data, enabling comparative analyses under varying conditions, such as dosage, administration route, and demographics. In addition, CT-ADE systematically collects all ADEs in the study population, including positive and negative cases. To provide a baseline for ADE prediction performance using the CT-ADE dataset, we conducted analyses using large language models (LLMs). The best LLM achieved an F1-score of 56%, with models incorporating treatment and patient information outperforming by 21%-38% those relying solely on the chemical structure. These findings underscore the importance of contextual information in ADE prediction and establish CT-ADE as a robust resource for safety risk assessment in pharmaceutical research and development.