Problem
Research questions and friction points this paper is trying to address.
Sepsis treatment compliance
Clinical protocol adherence
Evidence-based medicine
Neuro-symbolic AI
How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
🤖 AI Summary
This study addresses the challenge of balancing safety and accuracy in sepsis treatment compliance verification by proposing an expert-guided neuro-symbolic architecture. The method restricts large language models strictly to semantic normalization while integrating a Sugeno fuzzy inference system to encode clinical guidelines as graded scores, replacing traditional binary judgments. Validated on 2,438 cases, the system precisely identified antibiotic timing as a critical bottleneck, revealing a 36.7% Hour-1 bundle compliance rate and a 51% dropout rate due to elevated lactate levels. Furthermore, it demonstrated significant differences in ICU length of stay. By moving beyond rigid binary classification, this approach effectively enhances both the safety and clinical interpretability of automated compliance assessment in critical care settings.
📝 Abstract
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).
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-symbolic AI
Large Language Models
Fuzzy Inference System
Semantic Normalization
Clinical Compliance
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