Sepsis is a leading cause of in-hospital mortality, yet systematically evaluating temporal adherence to the Surviving Sepsis Campaign (SSC) bundle across large patient populations remains difficult due to semantic variability in electronic health records and the loss of clinical nuance inherent in binary pass/fail compliance judgments. We present an expert-guided neuro-symbolic pipeline that pairs LLM-based semantic normalization with a Sugeno fuzzy inference system encoding eight SSC bundle rules, producing graded per-episode compliance scores whose clinical decision boundaries are set through domain expert consultation. Applied to 2,438 sepsis episodes from MIMIC-IV v3.1, the dual-classifier normalization layer achieves substantial inter-system agreement with high embedding-based confirmation, resolving hundreds of clinically relevant drug strings that purely symbolic systems miss. The graded framework reveals that Hour-1 bundle failures, particularly antibiotic timing, are the dominant driver of low overall compliance, and that higher bundle adherence is associated with notably shorter ICU stays, with antibiotic delays beyond six hours increasing median stays by 61%. These results demonstrate that neuro-symbolic graded assessment can surface actionable compliance patterns that binary evaluation frameworks cannot capture.

The authors have declared no competing interest.

This study did not receive any funding

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The data used in this study were obtained from the Medical Information Mart for Intensive Care (MIMIC) database, which is maintained by the Massachusetts Institute of Technology (MIT) Laboratory for Computational Physiology in collaboration with Beth Israel Deaconess Medical Center. The use of the MIMIC database was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology, and the requirement for informed consent was waived due to the de-identified nature of the data.

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kroy2ua.edu

srahimi1ua.edu

subash.neupanemmc.edu

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The datasets analyzed during the current study are available from the MIMIC-IV v3.1 repository on PhysioNet. Access is controlled and requires completion of PhysioNet credentialing, required training, and acceptance of the data use agreement. The database is de-identified and available only to approved users.

https://physionet.org/content/mimiciv/3.1/