Publikation
A Neurosymbolic Approach for Constructing Planning Domain Models from Clinical Narratives
Ranveer Singh; Saurabh Mathur; Michael A. Skinner; Prasad Tadepalli; Kristian Kersting; Sriraam Natarajan
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2608.21186, Pages 1-9, ar, 2026.
Zusammenfassung
Surgical procedures such as laparoscopic appendectomy are
complex, high-stakes processes, yet formalizing their work-
flows for decision support remains a significant challenge.
Inducing probabilistic planning domain models in this set-
ting is particularly difficult due to the lack of structured event
data and the prevalence of implicit actions in clinical narra-
tives, 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 unstruc-
tured 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 precon-
ditions 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.