Bridging Neural Flexibility with Symbolic Rigor

Clinical compliance auditing, particularly for time-sensitive conditions like sepsis, often suffers from a trade-off between the high-dimensional pattern recognition of deep learning and the strict, interpretable requirements of medical protocols. This research introduces a neuro-symbolic pipeline designed to bridge this gap. By utilizing neural models to extract unstructured clinical data and symbolic logic to enforce expert-defined treatment guidelines, the system provides a verifiable audit trail that pure black-box models cannot offer.

Expert-Guided Compliance Auditing

The core of the pipeline relies on an expert-in-the-loop architecture. Instead of relying solely on end-to-end training, the system incorporates clinical knowledge as symbolic constraints. This ensures that the model's output aligns with established medical standards (such as the Surviving Sepsis Campaign guidelines). The symbolic layer acts as a filter or validator, flagging deviations from protocol and providing clinicians with the specific reasoning behind a compliance assessment. This approach reduces the risk of 'hallucinated' clinical justifications and ensures that the insights generated are actionable and medically sound.

Impact on Clinical Decision Support

By automating the compliance check process, this pipeline enables real-time monitoring of sepsis treatment. The neuro-symbolic approach allows for the processing of heterogeneous data sources—such as electronic health records, lab results, and clinical notes—while maintaining a high degree of transparency. This framework demonstrates that integrating symbolic reasoning into AI pipelines is not just a theoretical exercise but a practical necessity for high-stakes environments where accountability and explainability are paramount.