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Symptom Cascade Analyzer: A Graph-Theoretic Natural Language Processing Framework for Culturally-Adaptive Medical Diagnosis
Department of Computer Science, Dartmouth College, Hanover, NH, USA
- 1 Department of Computer Science, Dartmouth College, Hanover, NH, USA
Journal of Biomedical Science and Engineering·Volume 19 (2026)·Pages 8–14·Published 12 January 2026·DOI10.4236/jbise.2026.191002
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Abstract
We present the Symptom Cascade Analyzer (SCA), a natural language processing framework for culturally-adaptive medical diagnosis that integrates graph-theoretic symptom modeling, multilingual embeddings, and cultural adaptation layers. The framework incorporates graph entropy for rare-disease detection and demonstrates a 23% improvement in diagnostic accuracy for culturally specific symptom descriptions. Spectral clustering entropy analysis further enhances the identification of rare diseases. These results highlight SCA’s potential for deployment in multilingual, culturally diverse clinical environments.
KeywordsMultilingualNatural Language ProcessingMedical DiagnosisKnowledge GraphsCascade Analysis
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