
This study introduces the Atomic Vector Symbolic Architecture (Atomic VSA), a deterministic framework grounded in Hyperdimensional Computing (HDC) for clinical triage and explainable AI. Key Results:• 92.5% F1 Score on 25-category ICD-11 clinical triage (Winner-Take-All, no tuning)• 91.9% label recall for multi-label comorbidity detection• 11.97ms median inference latency on commodity CPU (15W)• Algebraically-traceable decisions enabling full audit trails The system employs 10,048-dimensional bipolar vectors with holographic reduced representations, enabling semantic composition through binding and bundling operations. We document an 8% accuracy ceiling due to semantic clones with 100% symptom overlap—a fundamental symptom-encoding limit requiring lab values for disambiguation. Atomic VSA is positioned as a complementary paradigm to neural networks for scenarios prioritizing deterministic inference, on-premise deployment, and regulatory compliance.
Healthcare AI, Interpretable Machine Learning, Explainable AI, Hyperdimensional Computing, Edge Computing, Clinical Decision Support, Vector Symbolic Architecture
Healthcare AI, Interpretable Machine Learning, Explainable AI, Hyperdimensional Computing, Edge Computing, Clinical Decision Support, Vector Symbolic Architecture
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