一种从临床叙事构建规划领域模型的神经符号方法
A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives
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中文总结 AI 辅助
针对临床叙事难以构建外科手术概率规划模型的问题，提出神经符号框架NSPIN，结合预训练LLM从2660份阑尾切除术记录生成的模型可泛化且符合临床实践。
中文摘要 AI 辅助
腹腔镜阑尾切除术等外科手术是复杂且高风险的流程，但将其工作流形式化以用于决策支持仍是重大挑战。在此场景下，由于缺乏结构化事件数据，且临床叙事中普遍存在隐式动作，诱导概率规划领域模型尤为困难，经验符号方法与大型语言模型（LLMs）均无法单独充分解决该问题。我们提出NSPIN，一种从非结构化临床叙事诱导概率规划领域模型的神经符号框架。该方法使用预训练LLM从原始文本提取并补全结构化事件序列，随后诱导PPDDL模型并在经验验证引导下，用LLM提出的修订优化其前提条件。我们在9位外科医生撰写的2660份腹腔镜阑尾切除术记录上评估该方法，结果显示NSPIN生成的模型可泛化至未见过的记录，且临床专家评审表明其诱导的知识与外科实践基本一致。
英文摘要
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.
