Advancing Clinical Trial Outcome Prediction with Domain-Specific Large Language Models

📄 arXiv: 2610.41710v1 📥 PDF

作者: Burak Bakkar, Hana Moalla, Mohamed Salem Jedidi, Haifa Ben Messaoud, Nicolas Lopez Carranza

分类: cs.AI

发布日期: 2026-10-30

💡 一句话要点

提出领域特定的大语言模型以提升临床试验结果预测

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 临床试验 结果预测 生物医学模型 语言模型 深度学习 药物开发 数据挖掘

📋 核心要点

  1. 现有的临床试验结果预测方法如HINT对自由文本的利用不足，限制了其预测能力。
  2. 本文提出使用BioBERT和BioMistral等领域特定语言模型，通过对临床试验注册文本进行建模来提升预测准确性。
  3. 实验结果表明，所有模型在ROC-AUC和PR-AUC指标上均超越HINT，显示出10-20%的相对提升。

📝 摘要（中文）

临床试验失败是药物开发中的主要障碍，导致高昂的成本和患者获取有效治疗的延迟。现有的结构化方法如层次交互网络（HINT）通过明确建模药物、疾病和方案之间的交互来预测试验结果，但对试验注册中的自由文本利用有限。本文评估了两个领域特定的语言模型BioBERT和BioMistral在阶段级结果预测中的表现，结果显示所有模型在ROC-AUC和PR-AUC上均优于HINT，表明预训练的生物医学表示已经编码了与试验结果相关的信号，且探测方法在计算上更具优势。

🔬 方法详解

问题定义：本文旨在解决临床试验结果预测中的信息利用不足问题，现有方法如HINT未能充分利用试验注册中的自由文本，导致预测性能受限。

核心思路：通过评估BioBERT和BioMistral等领域特定语言模型，利用其在生物医学领域的预训练知识，来增强临床试验结果的预测能力。

技术框架：研究采用了两种模型的微调和探测策略，BioBERT进行全微调，而BioMistral则采用低秩适应，模型输入为来自ClinicalTrials.gov的十个字段构建的文本。

关键创新：本文的主要创新在于展示了预训练的生物医学模型能够有效捕捉与试验结果相关的信号，并且探测方法在计算效率上优于传统的微调方法。

关键设计：实验中，模型在每个阶段的测试集上评估，BioMistral在第三阶段的ROC-AUC和PR-AUC分别达到0.772和0.901，且不同模型间的性能差异较小，未进行显著性测试。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，所有语言模型在ROC-AUC和PR-AUC上均超过HINT，具体表现为ROC-AUC在0.613至0.772之间，PR-AUC在0.649至0.901之间，表明相对提升幅度达到10-20%。

🎯 应用场景

该研究的潜在应用领域包括药物开发和临床试验设计，能够帮助制药公司和研究机构更准确地预测试验结果，从而优化资源配置和提高成功率。未来，随着模型的进一步优化和应用，可能会显著缩短新药上市的时间，提升患者的治疗体验。

📄 摘要（原文）

Clinical trial failures are a major barrier to efficient drug development, incurring substantial costs and delaying patient access to effective therapies. Structured approaches such as the Hierarchical Interaction Network (HINT) predict trial outcomes by explicitly modelling interactions between drugs, diseases and protocols, but make limited use of the free text in trial registrations. We evaluate two domain-specific language models, BioBERT (108M parameters) and BioMistral (7B parameters), for phase-level outcome prediction on the TOP benchmark (12,477 trials), representing each trial as text built from ten ClinicalTrials.gov fields. We compare fine-tuning (full fine-tuning for BioBERT, low-rank adaptation for BioMistral) with probing, in which the pretrained model is frozen and only a lightweight classifier is trained on its embeddings. On the held-out test set of each phase, all four language-model variants achieved higher ROC-AUC (0.613--0.772) and PR-AUC (0.649--0.901) than the published HINT results, corresponding to relative PR-AUC gains of 10--20\%. F1 differences were small and mixed, and no model, including HINT, clearly exceeded the F1 of a trivial classifier that predicts success for every trial. Under the configurations tested, probing performed comparably to fine-tuning: fine-tuned models scored highest on ROC-AUC and PR-AUC in Phases I and II, and frozen BioMistral embeddings in Phase III (ROC-AUC 0.772; PR-AUC 0.901), although most differences between variants were below 0.03 and were not tested for significance. These results suggest that pretrained biomedical representations already encode signal relevant to trial outcome, and that probing is a computationally cheaper alternative to fine-tuning for this task.