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

arXiv: https://arxiv.org/abs/2404.12827 · HTML: https://mlsanity.com/paper/2404.12827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 19 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Training Deep Nets with Sublinear Memory Cost](https://mlsanity.com/paper/1604.06174/) (2016)
- [MEDITRON-70B: Scaling Medical Pretraining for Large Language Models](https://mlsanity.com/paper/2311.16079/) (2023)
- [A Study of BFLOAT16 for Deep Learning Training](https://mlsanity.com/paper/1905.12322/) (2019)
- [ChemBERTa-2: Towards Chemical Foundation Models](https://mlsanity.com/paper/2209.01712/) (2022)

## Built on by (most cited first, 1 of 1)

- [Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning](https://mlsanity.com/paper/2602.22285/) (2026)

## Abstract (arXiv)

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.