IMPLEMENTATION OF A BASE OF RULES FOR DIFFERENTIAL DIAGNOSIS OF CLINICAL AND HEMATOLOGICAL SYNDROMES BASED ON MORPHOLOGICAL CLASSIFICATION ALGORITHM

Authors

DOI:

https://doi.org/10.37943/17BRIJ4866

Keywords:

rule base, artificial intelligence, automated systems, data analysis, medical information systems, differential diagnosis, clinical and hematological syndromes, morphological classification

Abstract

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

References

Amisha, Malik, P., Pathania, M., & Rathaur, V. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care, 8(7). https://doi.org/10.4103/jfmpc.jfmpc_440_19

Passamonti, F., Corrao, G., Castellani, G., Mora, B., Maggioni, G., Gale, R.P., & Della Porta, M.G. (2022). The future of research in hematology: Integration of conventional studies with real-world data and artificial intelligence. Blood Reviews. https://doi.org/10.1016/j.blre.2021.100914

Salib, C., Bhardwaj, S., Hussein, S., El Jamal, S., Petersen, B., Scigliano, E., Dembitzer, F.R., & Teruya-Feldstein, J. (2021). Digital AI in Hematology - Integration of the Scopio Labs x100 Scanner with Newly Implemented AI Capabilities into Routine Clinical Workflow. Blood, 138(Supplement 1). https://doi.org/10.1182/blood-2021-148821

Kaestner, L. (2020). Artificial intelligence meets hematology. Transfusion and Apheresis Science. https://doi.org/10.1016/j.transci.2020.102986

Çelik Ertuğrul, D., Toygar, Ö., & Foroutan, N. (2021). A rule-based decision support system for aiding iron deficiency management. Health Informatics Journal, 27(4). https://doi.org/10.1177 /14604582211066054

Kinney, E.L. (2022). Medical Expert Systems. https://journal.chestnet.org/article/S0012-3692(15)42851-X/fulltext

Laosai, J., & Chamnongthai, K. (2018). Deep-Learning-Based Acute Leukemia Classification Using Imaging Flow Cytometry and Morphology. In ISPACS 2018 - 2018 International Symposium on Intelligent Signal Processing and Communication Systems. https://doi.org/10.1109/ISPACS.2018.8923175

Yang, L.H., Ye, F.F., Liu, J., & Wang, Y.M. (2023). Belief rule-base expert system with multilayer tree structure for complex problems modeling. Expert Systems with Applications, 217. https://doi.org/10.1016/j.eswa.2023.119567

Troják, M., Šafránek, D., Pastva, S., & Brim, L. (2023). Rule-based modelling of biological systems using regulated rewriting. BioSystems, 225. https://doi.org/10.1016/j.biosystems.2023.104843

Rabbani, N., Kim, G. Y. E., Suarez, C. J., & Chen, J. H. (2022). Applications of machine learning in routine laboratory medicine: Current state and future directions. Clinical Biochemistry. https://doi.org/10.1016/j.clinbiochem.2022.02.011

Ryspekova, N.N. (2019). Rol tyazhelyh metallov v razvitii anemij. Vestnik Kazahskogo Nacionalnogo Medicinskogo Universiteta, 3(2), 46–51.

Sakko, Y., Turesheva, A., Gaipov, A., Aimagambetova, G., Ukybassova, T., Marat, A., Kaldygulova, L., Amanzholkyzy, A., Nogay, A., Khamidullina, Z., Mussenov, Y., Almawi, W. Y., & Atageldiyeva, K. (2023). Epidemiology of spontaneous pregnancy loss in Kazakhstan: A national population-based cohort analysis during 2014–2019 using the national electronic healthcare system. Acta Obstetricia et Gynecologica Scandinavica, 102(12). https://doi.org/10.1111/aogs.14669

Bazarbaeva, S., Dinmukhamedova, A., Tleubergenova, G., Rakhimzhanova, Z., Sembekova, K., Karbayeva, S., & Kuandykova, E. (2021). Morphofunctional and hematological characteristics of health in students from the northern and southern regions of kazakhstan. Open Access Macedonian Journal of Medical Sciences, 9. https://doi.org/10.3889/oamjms.2021.6434

Khozhayev, A., Valiyev, R., Sultan, M., Tassybekov, Z., Zhumabaeva, M., Ryspekkyzy, A., Amangeldinov, Y., & Assylbek, R. (2023). Organization Of Hematological Care in the Republic of Kazakhstan. Academics and Science Reviews Materials, 4.

Dong, N., Zhang, X., Wu, D., Hu, Z., Liu, W., Deng, S., & Ye, B. (2022). Medication Regularity of Traditional Chinese Medicine in the Treatment of Aplastic Anemia Based on Data Mining. Evidence-Based Complementary and Alternative Medicine, 2022. https://doi.org/10.1155/2022/1605359

Syed, K., Sleeman, W. C., Ghosh, P., Nalluri, J. J., Kapoor, R., Hagan, M., & Palta, J. (2020). Artificial intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics. Artificial Intelligence in Precision Health: From Concept to Applications. https://doi.org/10.1016/B978-0-12-817133-2.00002-1

Wang, Y., Liu, H., Wang, H., Wu, Y., Qiu, H., Qiao, C., Cao, L., Zhang, J., Li, J., Fan, L., & Wang, R. (2023). Enhancing morphological analysis of peripheral blood cells in chronic lymphocytic leukemia with an artificial intelligence-based tool. Hematological Oncology, 41(S2). https://doi.org/10.1002/hon.3165_506

Boadh, R., Chaudhary, K., Dahiya, M., Dogra, N., Rathee, S., Kumar, A., & Rajoria, Y. K. (2022). Analysis and investigation of fuzzy expert system for predicting the child anaemia. Materials Today: Proceedings, 56. https://doi.org/10.1016/j.matpr.2022.01.094

Appiahene, P., Chaturvedi, K., Asare, J.W., Donkoh, E.T., & Prasad, M. (2023). CP-AnemiC: A conjunctival pallor dataset and benchmark for anemia detection in children. Medicine in Novel Technology and Devices, 18. https://doi.org/10.1016/j.medntd.2023.100244

Karagül Yıldız, T., Yurtay, N., & Öneç, B. (2021). Classifying anemia types using artificial learning methods. Engineering Science and Technology, an International Journal, 24(1). https://doi.org/ 10.1016/j.jestch.2020.12.003

Al-qudah, R., & Suen, C.Y. (2021). Improving blood cells classification in peripheral blood smears using enhanced incremental training. Computers in Biology and Medicine, 131. https://doi.org/10.1016/j.compbiomed.2021.104265

Thinaharan, N., & Thiagarasu, V. (2019). A Rule Based Clinical Decision Support System for Healthcare Industry. Indian Journal of Science and Technology, 12(12), 1–10.

Uvaliyeva, I., Belginova, S., & Ismukhamedova, A. (2018). Development and implementation of the algorithm of differential diagnostics. In Proceedings of the International Conference «Application of Information and Communication Technologies-AICT 2018» (pp. 276–281). Almaty, Kazakhstan.

Belginova, S., Uvaliyeva, I., & Rustamov, S. (2019). The application of data mining methods for the process of diagnosing diseases. Journal of Theoretical and Applied Information Technology, 97(7).

Uvaliyeva, I., Belginova, S., Rustamov, S., & Ismukhamedova, A. (2019). Algorithm Diagnosis of Anemia on the basis of the Method of the Synthesis of the Decisive Rules. In 13th IEEE International Conference on Application of Information and Communication Technologies, AICT 2019 – Proceedings. https://doi.org/10.1109/AICT47866.2019.8981766

Medical Information Mart for Intensive Care. URL: https://mimic.physionet.org/ (date of request: 10.03.2024).

Uvaliyeva, I., Belginova, S., & Ismukhamedova, A. (2018). Informational and analytical system to diagnose anemia. In ACM International Conference Proceeding Series. https://doi.org/10.1145/3234698.3234716

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Published

2024-03-31

How to Cite

Uvaliуeva I., Ismukhamedova, A., Belginova, S., & Shaikhanova , A. (2024). IMPLEMENTATION OF A BASE OF RULES FOR DIFFERENTIAL DIAGNOSIS OF CLINICAL AND HEMATOLOGICAL SYNDROMES BASED ON MORPHOLOGICAL CLASSIFICATION ALGORITHM . Scientific Journal of Astana IT University, 17(17), 43–56. https://doi.org/10.37943/17BRIJ4866

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Information Technologies
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