How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
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Abstract (arXiv)
Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either paradigm alone. We present an expert-guided pipeline that constrains a large language model strictly to semantic normalization, mapping messy drug and microbiology strings onto a fixed clinical vocabulary, while a Sugeno fuzzy inference system reasons over the normalized events. The fuzzy layer encodes eight Surviving Sepsis Campaign bundle rules and replaces binary judgments with graded scores in [0,1]. Applied to 2,438 MIMIC-IV v3.1 sepsis episodes, it surfaces antibiotic timing as the most critical breakdown (mean 0.24, 13% within one hour), Hour-1 underperformance (mean 36.7%), a 51% elevated-lactate drop-off, and descriptive differences in ICU stay across compliance groups (3.8 versus 5.1 days).
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References · 7
- 2023ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluationcited by 85
- 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 39
- 2016Neuro-symbolic representation learning on biological knowledge graphscited by 25
- 2023CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Modelscited by 15
- 2023K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imagingcited by 6
- 2023Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphscited by 4
- 2025CLIN-LLM: A Safety-Constrained Hybrid Framework for Clinical Diagnosis and Treatment Generationcited by 2
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