用神经符号方法从手术病历自动构建可推理的流程模型
A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives
- 结合大模型与符号规则，从非结构化病历中提取并补全手术步骤
- 在2660份病历上验证，生成模型能准确泛化到未见文本
- 适合医疗流程建模、临床决策支持系统研发者使用
腹腔镜阑尾切除术等手术流程复杂且高风险，但将其工作流形式化以支持决策仍面临重大挑战。由于缺乏结构化事件数据，且临床叙述中普遍存在隐含操作，仅靠传统符号方法或大语言模型难以有效处理。本文提出NSPIN，一种从非结构化临床叙述中推导概率规划领域模型的神经符号框架。该方法利用预训练大模型提取并补全原始文本中的结构化事件序列，进而生成PPDDL模型，并通过大模型提出的修订建议结合实证验证进行前提条件优化。我们在9位外科医生撰写的2,660份腹腔镜阑尾切除术病历上评估该方法，结果表明所生成模型具备良好泛化能力，专家评审也显示其推导知识与实际手术实践高度一致。
原文摘要 · Abstract (English)
Surgical procedures such as laparoscopic appendectomy are complex, high-stakes processes, yet formalizing their workflows for decision support remains a significant challenge. Inducing probabilistic planning domain models in this setting is particularly difficult due to the lack of structured event data and the prevalence of implicit actions in clinical narratives, which neither empirical symbolic methods nor Large Language Models (LLMs) can adequately address on their own. We introduce NSPIN, a neurosymbolic framework for inducing probabilistic planning domain models from unstructured clinical narratives. Our method extracts and imputes structured event sequences from raw text using a pretrained LLM, then induces a PPDDL model and refines its preconditions with LLM-proposed revisions, guided by empirical validation. We evaluate the approach on 2,660 laparoscopic appendectomy notes written by 9 surgeons. NSPIN yields models that generalize to unseen notes, and expert clinical review indicates its induced knowledge is largely consistent with surgical practice.
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