Abstract
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.
Abstract (translated)
药物不良反应（ADEs）对临床研究和公共卫生产生重大影响，导致临床试验失败和医疗费用增加。准确预测和管理ADEs对提高更安全、更有效的药物开发至关重要。为了支持这一努力，我们引入了CT-ADE，一个专门为增强ADEs预测建模的新数据集。包含从临床试验结果中提取的超过12,000个实例，CT-ADE数据集整合了药物、患者人口和上下文信息，为多标签ADE分类任务提供了一个全面的资源，以开发高级预测模型。为了反映ADEs的复杂性，在MedDRA语义层的系统器官级别进行注释。使用基线模型进行初步分析已经取得了良好的成果，实现了73.33%的F1得分和81.54%的平衡准确率，突出了CT-ADE在提高ADE预测方面的潜力。CT-ADE为研究人员利用人工智能和机器学习加强患者安全并减轻ADEs对制药研究和开发产生影响提供了一个重要的工具。对使用CT-ADE数据集感兴趣的研究人员可以在该链接找到所有必要的资源。
URL
https://arxiv.org/abs/2404.12827
