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

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.临床试验是新型医疗疗法研发的关键环节，但通常伴随患者死亡、入组失败等风险，往往会浪费长达十余年的大量投入。将人工智能（AI）用于预测临床试验中的关键事件，有望为试验设计提供重要的决策参考与洞见。然而，由于复杂的数据采集与问题定义均需具备专业医学知识，迄今为止人工智能在该领域的应用仍受到阻碍。本文针对上述挑战，提出了一套经精心整理的23个适配人工智能的数据集，涵盖多模态输入特征，覆盖临床试验设计中的8项关键预测任务，具体包括试验时长预测、患者脱落率预测、严重不良事件预测、死亡率预测、试验获批结果预测、试验失败原因预测、药物剂量探索预测以及入选标准设计预测。此外，本文还为每项预测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发布将推动适用于临床试验设计的先进人工智能方法的研发，最终助力临床试验研究的发展，并加速医疗解决方案的开发进程。