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

• URL: http://arxiv.org/abs/2404.12827v3
• Date: Mon, 10 Mar 2025 09:51:28 GMT
• Title: An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
• Authors: Anthony Yazdani, Alban Bornet, Philipp Khlebnikov, Boya Zhang, Hossein Rouhizadeh, Poorya Amini, Douglas Teodoro,
• Abstract summary: Adverse drug events (ADEs) are a major safety issue in clinical trials.<n>We introduce CT-ADE, a dataset for multilabel ADE prediction in monopharmacy treatments.
• Score: 0.10051474951635876
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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