An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results
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arXiv
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| Format: | Preprint |
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2024
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| author | Yazdani, Anthony Bornet, Alban Khlebnikov, Philipp Zhang, Boya Rouhizadeh, Hossein Amini, Poorya Teodoro, Douglas |
| author_facet | Yazdani, Anthony Bornet, Alban Khlebnikov, Philipp Zhang, Boya Rouhizadeh, Hossein Amini, Poorya Teodoro, Douglas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_12827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results Yazdani, Anthony Bornet, Alban Khlebnikov, Philipp Zhang, Boya Rouhizadeh, Hossein Amini, Poorya Teodoro, Douglas Computation and Language 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. |
| title | An Evaluation Benchmark for Adverse Drug Event Prediction from Clinical Trial Results |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.12827 |