NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
For clinicians and AI developers, NEURON provides a scalable solution to enhance interpretability and trust in clinical AI predictions.
NEURON integrates SNOMED CT ontology with ML models and a RAG-grounded LLM to generate natural-language explanations for clinical predictions, improving AUC from 0.74-0.77 to 0.84-0.88 on MIMIC-IV for heart failure mortality prediction and outperforming raw SHAP in human-aligned metrics (0.85 vs. 0.50).
Clinical AI adoption is hindered by the black-box/grey-box nature of high-performing models, which lack the ontological grounding and narrative transparency required for professional-level explainability. We present NEURON, a neuro-symbolic system designed to enhance both predictive reliability and clinical interpretability. NEURON integrates SNOMED CT ontology-informed structural representations with machine learning models to bridge the gap between raw data and medical nomenclature. To facilitate human-aligned interaction, the system utilizes a Retrieval-Augmented Generation (RAG) grounded LLM layer to synthesize SHAP feature attributions and patient-specific clinical notes into coherent, natural-language explanations. Validated on the MIMIC-IV dataset for Acute Heart Failure mortality prediction, NEURON improved the AUC from 0.74-0.77 to 0.84-0.88 and significantly outperformed raw SHAP visualizations in human-aligned metrics (0.85 vs. 0.50). Our results demonstrate that NEURON offers a robust, scalable engineering solution for deploying trustworthy, human-centered connected health applications.