Enhancing Paediatric Diabetes Management: How Artificial Intelligence is Revolutionising Care

Reina Melani; Galih Samodra; Rosyid Al-Hakim

doi:10.57213/tjghpsr.v2i2.378

Authors

Reina Melani Universitas Harapan Bangsa
Galih Samodra Universitas Harapan Bangsa
Rosyid R. Al-Hakim Universitas Harapan Bangsa

DOI:

https://doi.org/10.57213/tjghpsr.v2i2.378

Keywords:

Artificial Intelligence, Glucose Monitoring, Paediatric Diabetes, Personalised Treatment, Predictive Algorithms

Abstract

Artificial intelligence (AI) is transforming paediatric diabetes management, offering innovative solutions for monitoring, treatment, and prediction. This mini-review explores how AI is being utilised to improve the care of children with diabetes mellitus, focusing on its application in glucose monitoring systems, predictive algorithms, and personalised treatment plans. The study synthesises recent advancements in AI technologies, examining their impact on enhancing the accuracy of diagnosis, reducing the burden on healthcare providers, and improving patient outcomes. Through a systematic review of the literature, key AI tools and models that have shown promise in paediatric diabetes care are identified. The findings highlight the potential of AI to revolutionise diabetes management, with implications for both clinical practice and future research. However, challenges remain in ensuring the ethical implementation and integration of these technologies into existing healthcare systems. The paper concludes with recommendations for advancing AI applications in this field, emphasising the need for continued innovation and collaboration between healthcare professionals and AI developers.

References

Agah, A. (2014). Medical Applications of Artificial Intelligence. CRC Press.

Al Hakim, R. R., & Saputro, T. A. (2021). Development of somatic cell nuclear transfer biotechnology for cloning of animals: a mini-review. Journal on Biology and Instruction, 1(1), 20–27. https://doi.org/10.12928/joubins.v1i1.3864

Al Hakim, R. R., Satria, M. H., Arief, Y. Z., Setiawan, A. D., Pangestu, A., & Hidayah, H. A. (2021). Artificial Intelligence for Thyroid Disorders: A Systematic Review. Science in Information Technology Letters, 2(2), 38–47. https://doi.org/10.31763/sitech.v2i2.694

Al-Hakim, R. R., Arief, Y. Z., Pangestu, A., Hidayah, H. A., Hamid, A. P., Andriand, A., Soelaiman, N. F., Arif, M., & Alrahman, M. H. A. (2023). Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis. SISTEMASI: Jurnal Sistem Informasi, 12(2), 415–424. https://doi.org/10.32520/stmsi.v12i2.2542

Almulla, M. A. (2021). Location-based expert system for diabetes diagnosis and medication recommendation. Kuwait Journal of Science, 48(1), 19–30. https://doi.org/10.48129/KJS.V48I1.8687

Alzyoud, M., Alazaidah, R., Aljaidi, M., Samara, G., Qasem, M. H., Khalid, M., & Al-Shanableh, N. (2024). Diagnosing diabetes mellitus using machine learning techniques. International Journal of Data and Network Science, 8(1), 179–188. https://doi.org/10.5267/J.IJDNS.2023.10.006

Ambilwade, R. P., Manza, R. R., & Gaikwad, B. P. (2014). Medical Expert Systems for Diabetes Diagnosis: A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 4(11), 2277.

American Diabetes Association. (2010). Diagnosis and classification of diabetes mellitus. In Diabetes Care (Vol. 33, Issue SUPPL. 1, p. S69). American Diabetes Association. https://doi.org/10.2337/dc10-S062

Barnes, R., & Zvarikova, K. (2021). Artificial Intelligence-enabled Wearable Medical Devices, Clinical and Diagnostic Decision Support Systems, and Internet of Things-based Healthcare Applications in COVID-19 Prevention, Screening, and Treatment. American Journal of Medical Research, 8(2), 9–22.

Braun, M., Hummel, P., Beck, S., & Dabrock, P. (2021). Primer on an ethics of AI-based decision support systems in the clinic. Journal of Medical Ethics, 47(12), e3–e3. https://doi.org/10.1136/MEDETHICS-2019-105860

Cai, T., Chen, C., Huang, T. H., & Ritter, F. E. (2021). What Makes A Good Reference Manager? A Quantitative Analysis of Bibliography Management Applications. 5th Asian CHI Symposium 2021, 64–69. https://doi.org/10.1145/3429360.3468183

CDC. (2023). Children and Diabetes. https://www.cdc.gov/diabetes/index.html

Cho, B. Y., Woodward, L., & Afflerbach, P. (2020). Qualitative approaches to the verbal protocol analysis of strategic processing. In Handbook of Strategies and Strategic Processing (pp. 373–392). Routledge. https://doi.org/10.4324/9780429423635-23/QUALITATIVE-APPROACHES-VERBAL-PROTOCOL-ANALYSIS-STRATEGIC-PROCESSING-BYEONG-YOUNG-CHO-LINDSAY-WOODWARD-PETER-AFFLERBACH

Creswell, J. W. (2012). Educational Research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson Education.

Danevska, L., Spiroski, M., Donev, D., Pop-Jordanova, N., & Polenakovic, M. (2016). How to Recognize and Avoid Potential, Possible, or Probable Predatory Open-Access Publishers, Standalone, and Hijacked Journals. Prilozi (Makedonska Akademija Na Naukite i Umetnostite. Oddelenie Za Medicinski Nauki), 37(2–3), 5–13. https://doi.org/10.1515/PRILOZI-2016-0011

Elston, D. M. (2019). Mendeley. In Journal of the American Academy of Dermatology (Vol. 81, Issue 5, p. 1071). Elsevier. https://doi.org/10.1016/j.jaad.2019.06.1291

Fernández-Sáez, A. M., Bocco, M. G., & Romero, F. P. (2010). SLR-Tool a tool for performing systematic literature reviews. ICSOFT 2010 - Proceedings of the 5th International Conference on Software and Data Technologies, 2(January), 157–166. https://doi.org/10.5220/0003003601570166

Ganie, S. M., Malik, M. B., & Arif, T. (2022). Performance analysis and prediction of type 2 diabetes mellitus based on lifestyle data using machine learning approaches. Journal of Diabetes & Metabolic Disorders, 21, 339–352. https://doi.org/10.1007/S40200-022-00981-W

Garcia, M. A., Gandhi, A. J., Singh, T., Duarte, L., Shen, R., Dantu, M., Ponder, S., & Ramirez, H. (2001). Esdiabetes (an expert system in diabetes). Proceedings of the Twelfth Annual CCSC South Central Conference on The Journal of Computing in Small Colleges, 166–175. https://doi.org/10.5555/374678.374762

García-Holgado, A., Marcos-Pablos, S., & García-Peñalvo, F. (2020). Guidelines for performing Systematic Research Projects Reviews. International Journal of Interactive Multimedia and Artificial Intelligence, 6(2), 9. https://doi.org/10.9781/IJIMAI.2020.05.005

Harmanto, S. (2022). Pengantar Kecerdasan Artifisial (2nd ed.). Penerbit Gunadarma.

Hauer, T. (2022). Importance and limitations of AI ethics in contemporary society. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/S41599-022-01300-7

Insani, M. I., Alamsyah, & Putra, A. T. (2018). Implementation of Expert System for Diabetes Diseases using Naïve Bayes and Certainty Factor Methods. Scientific Journal of Informatics, 5(2), 2407–7658.

International Diabetes Federation. (2021). IDF Diabetes Atlas 10th Edition. Https://Diabetesatlas.Org/. https://diabetesatlas.org/

Jamshed, S. (2014). Qualitative research method-interviewing and observation. Journal of Basic and Clinical Pharmacy, 5(4), 87. https://doi.org/10.4103/0976-0105.141942

Jethwani, P., Saboo, B., Jethwani, L., Kesavadev, J., Kalra, S., Sahay, R., Agarwal, S., & Hasnani, D. (2020). Management of children and adolescents having type 1 diabetes during COVID-19 pandemic in India: challenges and solutions. International Journal of Diabetes in Developing Countries, 40(3), 335–339. https://doi.org/10.1007/S13410-020-00865-W/TABLES/2

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/S42256-019-0088-2

Jumelle, A. K. L., Ispas, I., Thuernmler, C., Mival, O. H., Kosta, E., Casla, P., De Azúa, S. R., & González-Pinto, A. (2014). Ethical assessment in e-Health. 2014 IEEE 16th International Conference on E-Health Networking, Applications and Services, Healthcom 2014, 262–268. https://doi.org/10.1109/HEALTHCOM.2014.7001852

Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017). Machine Learning and Data Mining Methods in Diabetes Research. Computational and Structural Biotechnology Journal, 15, 104–116. https://doi.org/10.1016/J.CSBJ.2016.12.005

Kazemi, A., & Mohammadi, A. (2023). Presenting a Fuzzy Expert System for Diagnosis of Diabetes. International Journal of Decision Intelligence , 1(1), 17.

Khaleel, F. A., & Al-Bakry, A. M. (2021). Diagnosis of diabetes using machine learning algorithms. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2021.07.196

Kivimäki, M., Vahtera, J., Tabák, A. G., Halonen, J. I., Vineis, P., Pentti, J., Pahkala, K., Rovio, S., Viikari, J., Kähönen, M., Juonala, M., Ferrie, J. E., Stringhini, S., & Raitakari, O. T. (2018). Neighbourhood socioeconomic disadvantage, risk factors, and diabetes from childhood to middle age in the Young Finns Study: a cohort study. The Lancet Public Health, 3(8), e365–e373. https://doi.org/10.1016/S2468-2667(18)30111-7

Law, S. K., Au, D. C. T., Leung, A. W. N., & Xu, C. (2023). A mini-review of traditional Chinese medicines on liver diseases. Pharmacological Research - Modern Chinese Medicine, 7, 100243. https://doi.org/10.1016/J.PRMCM.2023.100243

Lewis, S. (2015). Qualitative Inquiry and Research Design: Choosing Among Five Approaches. Health Promotion Practice, 16(4), 473–475. https://doi.org/10.1177/1524839915580941

Marian, C. V. (2021). Artificial Intelligence Expert System Based on Continuous Glucose Monitoring (CGM) Data for Auto-Adaptive Adjustment Therapy Protocol - How to Make Sensors and Patients to Think Forward and Work Together? 2021 International Conference on E-Health and Bioengineering (EHB). https://doi.org/10.1109/EHB52898.2021.9657707

Masic, I. (2021). Predatory Journals and Publishers – Dilemmas: How to Assess it and How to Avoid it? Medical Archives, 75(5), 334. https://doi.org/10.5455/MEDARH.2021.75.328-334

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Altman, D., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., … Tugwell, P. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7). https://doi.org/10.1371/JOURNAL.PMED.1000097

Mythili, A., Kumar, K. D., Vivekananda, B., & Subrahmanyam, K. A. V. (2016). Clinical profile of type 1 diabetes mellitus in a tertiary care hospital. International Journal of Diabetes in Developing Countries, 36(2), 146–149. https://doi.org/10.1007/S13410-015-0366-4/TABLES/6

Nagaraj, P., & Deepalakshmi, P. (2022). An intelligent fuzzy inference rule-based expert recommendation system for predictive diabetes diagnosis. International Journal of Imaging Systems and Technology, 32(4), 1373–1396. https://doi.org/10.1002/IMA.22710

Naz, H., & Ahuja, S. (2020). Deep learning approach for diabetes prediction using PIMA Indian dataset. Journal of Diabetes and Metabolic Disorders, 19, 391–403. https://doi.org/10.1007/S40200-020-00520-5/METRICS

Noviandi, N. (2018). Implementasi Algoritma Decision Tree C4.5 untuk Prediksi Penyakit Diabetes. Jurnal INOHIM, 6(1), 1–5. www.kaggle.com/uciml/pima-indians-diabetes-database

Purwono, P., Burhan, A., Nisa, K., Wibisono, S. K., Setiawan Mangkunegara, I., Dewi, P., Ma’Arif, A., & Suwarno, I. (2023). Prototype Design of Smart Diabetic Shoes with Lora Module Communication. 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), 863–868. https://doi.org/10.1109/ICCOSITE57641.2023.10127713

Staples, M., & Niazi, M. (2007). Experiences using systematic review guidelines. Journal of Systems and Software, 80(9), 1425–1437. https://doi.org/10.1016/J.JSS.2006.09.046

Sulastri, F., Sunge, A. S., & Achmad, L. I. (2023). Komparasi Akurasi Kernel Support Vector Machine Dalam Prediksi Diabetes. Prosiding SAINTEK: Sains Dan Teknologi, 2(1), 555–562. https://www.jurnal.pelitabangsa.ac.id/index.php/SAINTEK/article/view/2225

Tavares, L. D., Manoel, A., Donato, T. H. R., Cesena, F., Minanni, C. A., Kashiwagi, N. M., da Silva, L. P., Amaro, E., & Szlejf, C. (2022). Prediction of metabolic syndrome: A machine learning approach to help primary prevention. Diabetes Research and Clinical Practice, 191, 110047. https://doi.org/10.1016/J.DIABRES.2022.110047

Toh, C., & P. Brody, J. (2021). Applications of Machine Learning in Healthcare. In Smart Manufacturing - When Artificial Intelligence Meets the Internet of Things. IntechOpen. https://doi.org/10.5772/INTECHOPEN.92297

Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. In Nature Medicine (Vol. 25, Issue 1, pp. 44–56). Nature Publishing Group. https://doi.org/10.1038/s41591-018-0300-7

You, S., & Kang, M. (2020). A Study on Methods to Prevent Pima Indians Diabetes using SVM. Korea Journal of Artificial Intelligence, 8(2), 7–10.

Yu, G., Chen, Z., Wu, J., & Tan, Y. (2021). Medical decision support system for cancer treatment in precision medicine in developing countries. Expert Systems with Applications, 186, 115725. https://doi.org/10.1016/J.ESWA.2021.115725

Yusianto, W., Wahyuni, R. T. D., & Jamaludin, J. (2024). Knowledge and Compliance of Diabetes Mellitus Patients in Following the Chronic Disease Management Program (Prolanis). Menara Journal of Health Science, 3(2), 302–312. https://doi.org/10.36929/JPK.V7I2.132