# A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives

arXiv: https://arxiv.org/abs/2608.21186 · HTML: https://mlsanity.com/paper/2608.21186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?](https://mlsanity.com/paper/2412.14232/) (2024)
- [On the Limit of Language Models as Planning Formalizers](https://mlsanity.com/paper/2412.09879/) (2024)
- [PROC2PDDL: Open-Domain Planning Representations from Texts](https://mlsanity.com/paper/2403.00092/) (2024)
- [SLR: Automated Synthesis for Scalable Logical Reasoning](https://mlsanity.com/paper/2506.15787/) (2025)
- [SQUiD: Synthesizing Relational Databases from Unstructured Text](https://mlsanity.com/paper/2505.19025/) (2025)
- [Graph Neural Network Based Action Ranking for Planning](https://mlsanity.com/paper/2412.04752/) (2024)
- [Local-to-Global Logical Explanations for Deep Vision Models](https://mlsanity.com/paper/2601.13404/) (2026)

## Abstract (arXiv)

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.