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

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arxiv 2407.00631 v3 pith:KKZUA2FT submitted 2024-06-30 cs.LG cs.AI

Jintai Chen , Yaojun Hu , Mingchen Cai , Yingzhou Lu , Yue Wang , Xu Cao , Miao Lin , Hongxia Xu
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Jian Wu Cao Xiao Jimeng Sun Yuqiang Li Lucas Glass Kexin Huang Marinka Zitnik Tianfan Fu
This is my paper · ORCID
classification cs.LGcs.AI
keywords trialclinicaldatasetsdesignmedicalartificialchallengesdevelopment
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

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  1. $\texttt{AMEND++}$: Benchmarking Eligibility Criteria Amendments in Clinical Trials

    cs.CL 2026-01 conditional novelty 6.0 of 10

    Initial eligibility-criteria text is weakly predictive of later protocol amendments, and a change-aware masked-language-modeling pretraining step improves prediction by about 1.4% relative AUROC.

  2. AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents

    cs.LG 2025-06 reject novelty 6.0 of 10

    AutoCT achieves test ROC-AUC 0.753, 0.639, and 0.702 on Phase I/II/III trial approval prediction using 100-sample subsets and LLM-generated features.

  3. FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation

    cs.LG 2024-12 conditional novelty 3.0 of 10

    Finetuning Llama 3.1 8B/70B with QLoRA on financial datasets improves accuracy over base models while reducing GPU memory, though the evaluation lacks baselines and the headline gain is imprecise.

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