让大模型系统化推理临床试验匹配规则，提升精准度。
Controlled LLM-based Reasoning for Clinical Trial Retrieval
- 用集合引导的推理框架增强大模型对医疗标准的逻辑判断
- 在TREC 2022数据集上达到NDCG@10 0.693、Precision@10 0.73
- 适合医疗信息检索与智能匹配场景的科研人员参考
将患者匹配到临床试验需要系统性且有逻辑地解读文档，这要求具备高水平医学背景知识，并处理一套复杂的预定义纳入标准。同时，该解读过程需在海量临床试验知识库上实现规模化运行。本文提出一种可扩展的方法，通过引入集合引导的推理机制，增强大模型在医学纳入标准集合上的系统化推理能力，并在真实世界案例中进行评估。实验在TREC 2022临床试验数据集上进行，结果优于现有最先进方法：NDCG@10达0.693，Precision@10达0.73。
原文摘要 · Abstract (English)
Matching patients to clinical trials demands a systematic and reasoned interpretation of documents which require significant expert-level background knowledge, over a complex set of well-defined eligibility criteria. Moreover, this interpretation process needs to operate at scale, over vast knowledge bases of trials. In this paper, we propose a scalable method that extends the capabilities of LLMs in the direction of systematizing the reasoning over sets of medical eligibility criteria, evaluating it in the context of real-world cases. The proposed method overlays a Set-guided reasoning method for LLMs. The proposed framework is evaluated on TREC 2022 Clinical Trials, achieving results superior to the state-of-the-art: NDCG@10 of 0.693 and Precision@10 of 0.73.
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