NEURON: A Neuro-symbolic System for Grounded Clinical Explainability

• URL: http://arxiv.org/abs/2605.01189v1
• Date: Sat, 02 May 2026 02:00:02 GMT
• Title: NEURON: A Neuro-symbolic System for Grounded Clinical Explainability
• Abstract summary: NEURON is a neuro-symbolic system designed to enhance both predictive reliability and clinical interpretability.<n>Our results demonstrate that NEURON offers a robust, scalable engineering solution for deploying trustworthy, human-centered connected health applications.
• Score: 5.893027587750518
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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