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.
References
[1]
Koleck, T.A., Dreisbach, C., Bourne, P.E. and Bakken, S. (2019) Natural Language Processing of Symptoms Documented in Free-Text Narratives of Electronic Health Records: A Systematic Review. JournaloftheAmericanMedicalInformaticsAssociation, 26, 364-379. https://doi.org/10.1093/jamia/ocy173
[2]
Hossain, E., Rana, R., Higgins, N., Soar, J., Barua, P.D., Pisani, A.R., et al. (2023) Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-Making: A Systematic Review. ComputersinBiologyandMedicine, 155, Article ID: 106649. https://doi.org/10.1016/j.compbiomed.2023.106649
[3]
Tahabi, F.M., Storey, S. and Luo, X. (2023) SymptomGraph: Identifying Symptom Clusters from Narrative Clinical Notes Using Graph Clustering. Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, Tallinn Estonia, 27-31 March 2023, 518-527. https://doi.org/10.1145/3555776.3577685
[4]
Liu, C.C., Gurevych, I. and Korhonen, A. (2025) Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art. TransactionsoftheAssociationforComputationalLinguistics, 13, 652-689. https://doi.org/10.1162/tacl_a_00760
[5]
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., et al. (2020) Unsupervised Cross-Lingual Representation Learning at Scale. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5-10 July 2020, 8440-8451. https://doi.org/10.18653/v1/2020.acl-main.747
[6]
Jannat, A. (2025) Machine Learning Methods for Rare Disease Detection: A Systematic Review. Master’s Thesis, University of Eastern Finland.
[7]
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
[8]
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P. and Bengio, Y. (2018) Graph Attention Networks. arXiv: 1710.10903.
[9]
McMahan, H.B., Moore, E., Ramage, D., Hampson, S. and Aguera y Arcas, B. (2017) Communication-Efficient Learning of Deep Networks from Decentralized Data. arXiv: 1602.05629.
