arXiv:2608.13617v1 Announce Type: new 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).
How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
A new expert-guided neuro-symbolic pipeline that combines a large language model with a Sugeno fuzzy inference system to assess compliance with sepsis treatment protocols found that antibiotic timing is the most critical breakdown, with a mean compliance score of 0.24 and only 13% of patients receiving antibiotics within one hour. Applied to 2,438 MIMIC-IV v3.1 sepsis episodes, the pipeline also revealed Hour-1 underperformance (mean 36.7%), a 51% drop-off in elevated-lactate measurement, and longer ICU stays for non-compliant patients (5.1 vs 3.8 days). The research, posted on arXiv (2608.13617v1), encodes eight Surviving Sepsis Campaign bundle rules into graded scores in [0,1].
Run your AI side-project on zahid.host
EU-based hosting, git-push deploys, automatic HTTPS, no cold starts. Free tier with a custom domain — perfect for shipping the agent you just read about.