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Swartz, J.B. (1998) An Entropy-Based Algorithm for Detecting Clusters of Cases and Controls and Its Comparison with a Method Using Nearest Neighbours. Health & Place, 4, 67-77.
https://doi.org/10.1016/s1353-8292(97)00026-9

has been cited by the following article:

• TITLE: Symptom Cascade Analyzer: A Graph-Theoretic Natural Language Processing Framework for Culturally-Adaptive Medical Diagnosis

  AUTHORS: Felix Davis

  KEYWORDS: Multilingual, Natural Language Processing, Medical Diagnosis, Knowledge Graphs, Cascade Analysis

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.19 No.1, January 12, 2026

  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.