arXiv 2407.006312025-06-15cs.LGcs.AI

TrialBench：面向多模态人工智能的临床试验数据集

TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets

AI Thrust, Information Hub, HKUST(GZ)（香港科技大学（广州）人工智能 thrust 与信息中心）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）；School of Medicine, Stanford University（斯坦福大学医学院）；Computer Science Department, UIUC（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University（广东省人民医院（广东省医学科学院）医学大数据中心，南方医科大学）；Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University（浙江大学人工智能医学创新研究院，浙江大学药学院）；The Second Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第二医院）；GE HealthCare（通用电气医疗）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；IQVIA；Computer Science Department, Stanford University（斯坦福大学计算机科学系）；Informatics, Harvard Medical School, Harvard University（哈佛医学院，哈佛大学informatics部门）；State Key Laboratory for Novel Software Technology at Nanjing University, School of Computer Science, Nanjing University（南京大学新型软件技术国家重点实验室，南京大学计算机科学学院）

Jintai Chen, Yaojun Hu, Mingchen Cai, Yingzhou Lu, Yue Wang, Xu Cao, Miao Lin, Hongxia Xu, Jian Wu, Cao Xiao, Jimeng Sun, Yuqiang Li, Lucas Glass, Kexin Huang, … 展开作者

Jintai Chen, Yaojun Hu, Mingchen Cai, Yingzhou Lu, Yue Wang, Xu Cao, Miao Lin, Hongxia Xu, Jian Wu, Cao Xiao, Jimeng Sun, Yuqiang Li, Lucas Glass, Kexin Huang, Marinka Zitnik, Tianfan Fu

备注：: accepted by Nature Scientific Data

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AI总结：

针对AI参与临床试验设计面临的数据收集复杂、问题定义需医学专业知识的难题，本文推出包含23个多模态AI就绪数据集的TrialBench，覆盖8项关键预测挑战并提供验证方法，以推动相关AI研究与医疗方案开发。

摘要：

临床试验对开发新的医疗疗法至关重要，但通常存在患者死亡、入组失败等风险，会浪费长达十余年的巨大投入。应用人工智能（AI）预测临床试验中的关键事件，有望为指导试验设计提供洞见。然而，复杂的数据收集以及需要医学专业知识的问题定义，一直阻碍着AI至今的参与。本文针对这些挑战，推出了一套包含23个精心整理的、可供AI直接使用的综合数据集，涵盖多模态输入特征以及临床试验设计中的8项关键预测挑战，包括试验时长预测、患者脱落率预测、严重不良事件预测、死亡率预测、试验审批结果预测、试验失败原因预测、药物剂量发现、入组标准设计。此外，我们为每项任务提供了基础验证方法，以确保数据集的可用性与可靠性。我们预计这类开放获取数据集的推出将推动用于临床试验设计的先进AI方法的发展，最终推进临床试验研究并加速医疗解决方案的开发。

英文摘要：

Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。