Symptom Cascade Analyzer: A Graph-Theoretic Natural Language Processing Framework for Culturally-Adaptive Medical Diagnosis
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.
Fields of Study & Subjects
Artificial Intelligence in Healthcare
Biomedical Text Mining and Ontologies
Machine Learning in Healthcare
Publication details
- Journal / source
- Journal of Biomedical Science and Engineering
- Volume
- 19
- Issue
- 01
- Pages
- 8–14
- Publication date
- 1 January 2026