Adverse drug events (ADEs) significantly impact clinical research and public
health, contributing to failures in clinical trials and leading to increased
healthcare costs. The accurate prediction and management of ADEs are crucial
for improving the development of safer, more effective medications, and
enhancing patient outcomes. To support this effort, we introduce CT-ADE, a
novel dataset compiled to enhance the predictive modeling of ADEs. Encompassing
over 12,000 instances extracted from clinical trial results, the CT-ADE dataset
integrates drug, patient population, and contextual information for multilabel
ADE classification tasks in monopharmacy treatments, providing a comprehensive
resource for developing advanced predictive models. To mirror the complex
nature of ADEs, annotations are standardized at the system organ class level of
the Medical Dictionary for Regulatory Activities (MedDRA) ontology. Preliminary
analyses using baseline models have demonstrated promising results, achieving
73.33% F1 score and 81.54% balanced accuracy, highlighting CT-ADE's potential
to advance ADE prediction. CT-ADE provides an essential tool for researchers
aiming to leverage the power of artificial intelligence and machine learning to
enhance patient safety and minimize the impact of ADEs on pharmaceutical
research and development. Researchers interested in using the CT-ADE dataset
can find all necessary resources at this https URL

为了提高药物研发的安全性和患者预后，本研究介绍了 CT-ADE 数据集，该数据集基于临床试验结果提供了药物、患者人群和环境信息，并以 MedDRA 本体论的系统器官类别层次进行多标签 ADE 分类任务的标准化注释，通过基准模型的初步分析获得了令人满意的结果，展示了 CT-ADE 在 ADE 预测方面的潜力。CT-ADE 为希望利用人工智能和机器学习提高患者安全性并减少 ADE 对药物研发的影响的研究人员提供了一个重要工具。