CLUSTERING AND CLASSIFICATION OF DISEASES USING STOCHASTIC DYNAMIC OPTIMIZATION

This study presents a new approach to the optimization of Natural Language Processing (NLP) techniques for medical entity recognition and disease classification. By leveraging patient queries and PubMed article abstracts, the research uses advanced extraction methods to identify biomedical entities and diseases from medical texts. Diseases are grouped using a combination of TF-IDF and K-means clustering, and classification models are then applied to predict disease clusters based on known entities. A key innovation of this work is the use of Stochastic Dynamic Optimization to fine-tune parameters, significantly enhancing clustering and classification performance.

Experimental results demonstrate that the proposed method improves the accuracy of extraction and classification, outperforming traditional methods in terms of precision and scalability. This scalable and efficient approach to biomedical data analysis has the potential to support future clinical decision-making, enable personalized medicine, and provide valuable healthcare insights, ultimately contributing to improved patient outcomes and more effective research workflows.

1. Choudhary P. An Intelligent Chatbot Design and Implementation Model Using Long Short-Term Memory with Recurrent Neural Networks and Attention Mechanism / P. Choudhary, S. Chauhan // Decision Analytics Journal. – 2023. – Vol. 9, № 100359. https://doi.org/10.1016/j.dajour.2023.100359.

2. Online Biomedical Named Entities Recognition by Data and Knowledge-Driven Model / Cao Lulu et al // Artificial Intelligence in Medicine. – 2024. – Vol. 150, № 102813. https://doi.org/10.1016/j.artmed.2024.102813.

3. Exploring Named Entity Recognition and Relation Extraction for Ontology and Medical Records Integration / D.P. da Silva et al // Informatics in Medicine Unlocked. – 2023. – Vol. 43, № 101381. https://doi.org/10.1016/j.imu.2023.101381.

4. Medical Named Entity Recognition (MedNER): A Deep Learning Model for Recognizing Medical Entities (Drug, Disease) from Scientific Texts / Miah M.S. Ullah et al // IEEE EUROCON 2023 – 20th International Conference on Smart Technologies. – 2023. – P. 158-162. https://doi.org/10.1109/EUROCON56442.2023.10199075.

5. Named Entity Recognition Based on Boundary Enhanced for Chinese Electronic Medical Records / X. Chen et al // 2023 12th International Conference of Information and Communication Technology (ICTech). – 2023. – P. 73-77. https://doi.org/10.1109/ICTECH58362.2023.00025.

6. Tang L. Named Entity Recognition in Chinese Medical Texts Based on RoBERTa-WWMIDCNN-CRF / L. Tang, J. Kong, L. Xu // 2024 IEEE 25th China Conference on System Simulation Technology and Its Application (CCSSTA). – 2024. – P. 315-319. https://doi.org/10.1109/CCSSTA62096.2024.10691735.

7. Zhang Y. Named Entity Recognition of Medical Examination Reports Based on BiLSTM+CRF Model / Y. Zhang, F. Zhang // 2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT). – 2023. – P. 340-344. https://doi.org/10.1109/AINIT59027.2023.10212675.

8. Patient Clustering Optimization with K-Mean in Healthcare Data Analysis / A.K. Rai et al // 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). – 2023. – P. 1-7. https://doi.org/10.1109/ICAIIHI57871.2023.10489428.

9. Ershadi Mohammad Mahdi. Applications of Dynamic Feature Selection and Clustering Methods to Medical Diagnosis / Ershadi Mohammad Mahdi, Seifi Abbas // Applied Soft Computing. – 2022. – Vol. 126, № 109293. https://doi.org/10.1016/j.asoc.2022.109293.

10. A Large-Scale Dataset of Patient Summaries for Retrieval-Based Clinical Decision Support Systems / Z. Zhao et al // Scientific Data. – 2023. – Vol. 10, № 909. https://doi.org/10.1038/s41597-023-02814-8.