用大模型将临床试验文本转为可计算逻辑，提升自动化分析能力
Scaling Up Formal Representation of Clinical Trial Protocols in Ensemble Logic Using LLMs: A Preliminary Study
- 用大模型自动将试验文本转为时间逻辑表达式
- 23个真实试验的转换保真度较高，语义相似度达标
- 适合医学信息学与可计算临床研究者参考
临床试验协议通常以非结构化自由文本记录，阻碍了自动化推理、队列发现和试验模拟。关键的时间性特征（如动态入组标准和事件时序约束）因缺乏形式化结构而难以捕捉。尽管时序集成逻辑（TEL）能有效建模这些要素，但手动编码仍存在严重瓶颈。本文提出CT-TEL流程：利用大语言模型（LLMs）构建可扩展的流水线，将叙述性试验协议转化为TEL公式。我们对ClinicalTrials.gov上的23个真实试验应用该方法，并通过回译法评估转换保真度——使用LLM将生成的TEL公式还原为自然语言，再与原始文本比较语义相似性。结果显示，语义保留良好，表明大模型可能为非正式协议向可计算逻辑的映射提供可行路径，初步支持了对应作者倡导的“符号化生物医学”范式下临床试验仿真的规模化实现。
原文摘要 · Abstract (English)
The reliance on unstructured free text for documenting clinical trial protocols creates a significant barrier to automated reasoning, cohort discovery, and trial simulation. The lack of formal structure obscures critical temporal phenotypes, such as dynamic eligibility criteria and event timing constraints. Although Temporal Ensemble Logic (TEL) offers an expressive framework for modeling these elements, manual encoding remains a prohibitive bottleneck. We introduce the CT-TEL workflow: a scalable pipeline leveraging Large Language Models (LLMs) to translate narrative clinical protocols into TEL formulas. We applied CT-TEL to generate logical models for 23 real-world trials from ClinicalTrials.gov. We evaluated translation fidelity via a back-translation approach, using LLMs to convert TEL formulas back into natural language and measuring semantic similarity against source texts. The resulting semantic retention suggests that LLMs may offer a pathway for mapping informal protocols to computable logic, providing preliminary evidence toward scalable clinical trial emulation within the emerging "Symbolic Biomedicine" paradigm championed by the corresponding author.
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