A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives

2026-08-21

AI Summary

研究通过NSPIN神经符号框架从临床叙述中提取并推断出概率规划领域模型，以解决手术流程形式化难题。

Abstract

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.

Problem

• clinical narratives
• probabilistic planning domain models
• unstructured data
• implicit actions

Innovation

• Neurosymbolic Framework
• Probabilistic Planning Domain Models
• Clinical Narratives
• LLM
• PPDDL