发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于大语言模型的端到端方法，将NICE临床指南转换为可执行模型，在胰腺癌案例中F1达82.5%，证明了自动化生成可计算临床指南的可行性。
AI 中文摘要
引言：NICE指南为临床护理提供基于证据的建议，但大多仍以非结构化自然语言形式存在。现有将其转换为可计算表示的方法通常聚焦于单个疾病，需要大量手动编码，且无法扩展。大语言模型（LLMs）或可实现大部分此类转换的自动化。方法：我们提出一种端到端方法，将文本临床指南转换为可执行模型，该模型能生成可解释、针对特定患者的建议。基于上下文示例的逐步LLM转换会产生可人工检查的中间产物。我们将该方法应用于NICE胰腺癌和肺癌指南，采用专家审查评估规则一致性，并在20个患者 vignette（临床案例）上评估可执行胰腺癌模型。结果：专家审查显示源指南与生成的可执行模型之间具有强一致性。大多数差异为部分遗漏而非逻辑错误，幻觉或根本错误的规则较为罕见。在患者临床案例上，该可执行模型的F1分数达到82.5%。结论：LLMs可将自然语言形式的NICE指南转换为可解释、可执行的模型，这些模型保留指南结构，支持透明检查与修改，并能生成针对特定患者的建议。这些发现证明了可扩展自动化生成可计算临床指南的可行性。
英文摘要
Introduction: NICE guidelines provide evidence-based recommendations for clinical care but remain largely in unstructured natural language. Existing approaches to converting them into computable representations often focus on individual diseases, require substantial manual encoding, and do not scale. Large language models (LLMs) may enable much of this translation to be automated. Methods: We present an end-to-end approach that converts textual clinical guidelines into executable models capable of generating explainable, patient-specific recommendations. A stepwise LLM-based transformation with in-context examples produces human-inspectable intermediate artifacts. We apply the approach to NICE pancreatic and lung cancer guidelines, use expert review to assess rule alignment, and evaluate the executable pancreatic cancer model on 20 patient vignettes. Results: Expert review showed strong alignment between the source guidelines and generated executable models. Most discrepancies were partial omissions rather than incorrect logic, while hallucinated or fundamentally incorrect rules were rare. On the patient vignettes, the executable model achieved an F1 score of 82.5%. Conclusion: LLMs can transform natural-language NICE guidelines into interpretable, executable models that preserve guideline structure, support transparent inspection and modification, and generate patient-specific recommendations. These findings demonstrate the feasibility of scalable automated generation of computable clinical guidelines.
Comments18 pages. Published in Learning Health Systems (2026)
Journal refLearning Health Systems, 2026, 10(4):e70114. PubMed lists the article in volume 10, issue 4
