脓毒症治疗的依从性如何？一种专家指导的神经符号管道用于生成临床依从性见解
How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
- The University of Alabama（阿拉巴马大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对脓毒症治疗依从性验证问题，提出专家指导的神经符号管道，结合大语言模型语义规范化与Sugeno模糊推理系统，在MIMIC-IV数据集上揭示了抗生素时机等关键依从性问题及相关临床差异。
AI中文摘要：
验证临床护理是否遵循循证方案是一个天然的神经符号问题，但安全关键的环境单独击败了任一范式。我们提出一种专家指导的管道，将大语言模型严格约束为语义规范化，将杂乱的药物和微生物学字符串映射到固定临床词汇表，同时Sugeno模糊推理系统对规范化事件进行推理。该模糊层编码8项脓毒症存活运动集束规则，并用[0,1]区间内的分级评分替代二元判断。应用于2438个MIMIC-IV v3.1脓毒症发作，其揭示抗生素时机是最关键的失效点（均值0.24，1小时内占13%）、1小时达标不足（均值36.7%）、乳酸升高下降51%，以及依从性组间ICU住院天数的描述性差异（3.8天对比5.1天）。
英文摘要：
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).
