Tinjauan Supervised Reinforcement Learning pada Tindakan Medis Penyakit Diabetes Melitus
Review of Supervised Reinforcement Learning on Medical Actions for Diabetes Mellitus
DOI:
https://doi.org/10.57152/malcom.v4i3.1363Keywords:
AI, Deep Reinforcement Learning, Diabetis Melitus, Supervised Reinforcement Learning, Tindakan MedisAbstract
Diabetes Melitus (DM) merupakan penyakit kronis yang memerlukan pengelolaan medis yang berkelanjutan. Pengelolaan pengendalian penyakit diabetes bergantung pada kadar glukosa dalam darah guna mengambil tindakan yang tepat agar dapat mencegah kadar glukosa darah menjadi terlalu rendah atau tinggi. Dalam konteks perawatan medis DM, penggunaan teknologi pembelajaran mesin, khususnya Supervised Reinforcement Learning (SRL) telah mengahadirkan pendekatan yang inovatif. Penelitian ini bertujuan untuk menyelidiki dan merangkum beberapa karya ilmiah yang membahas tentang penerapan SRL dalam konteks tindakan medis untuk penyakit DM. Beberapa percobaan dilakukan oleh para peneliti dengan menggunakan data dari pasien diabetes untuk menentukan parameter model yang optimal, melakukan simulasi dan studi validasi secara real-time sehinga dapat memberikan wawasan lebih lanjut tentang penerapan praktis model pembelajaran penguatan dalam pengaturan klinis. Melalui SRL, agen pembelajaran dapat menggabungkan umpan balik lingkungan dengan informasi eksplisit dari supervisor untuk menghasilkan keputusan yang optimal dalam pengelolaan DM. Dalam makalah ini, penulis menganalisis kajian literatur terkait penerapan SRL pada pengelolaan medis DM, serta mengeksplorasi potensi dan tantangan yang terkait dengan penggunaan pendekatan ini dalam praktik klinis
References
R. Kumar, P. Saha, Y. Kumar, S. Sahana, A. Dubey, And O. Prakash, "A Review On Diabetes Mellitus: Type 1 & Type 2," World Journal of Pharmacy and Pharmaceutical Sciences, Vol. 9, No. 10, Pp. 838-850, Oct. 2020. Issn 2278 - 4357.
M. Tejedor, A. Z. Woldaregay, and F. Godtliebsen, "Reinforcement learning application in diabetes blood glucose control: A systematic review," Artificial Intelligence in Medicine, vol. 104, p. 101836, Apr. 2020.
F. Zohora, M. H. Tania, M. S. Kaiser, and M. Mahmud, "Forecasting the Risk of Type II Diabetes using Reinforcement Learning," presented at the 2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Kitakyushu, Japan, Aug. 26-29, 2020, doi: 10.1109/ICIEVicIVPR48672.2020.9306653.
C. Shiranthika, K.-W. Chen, C.-Y. Wang, C.-Y. Yang, B. H. Sudantha, and W.-F. Li, "Supervised Optimal Chemotherapy Regimen Based on Offline Reinforcement Learning," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 9, pp. 4763-4772, Sep. 2022, doi: 10.1109/JBHI.2022.3183854.
S. Neaz, N. Hussain, M. F. Hossain, and T. Rahman, "Diabetes Mellitus: Insights from Epidemiology, Biochemistry, Risk Factors, Diagnosis, Complications and Comprehensive Management," Diabetology 2021, vol. 2, pp. 36–50, 2021. MDPI Journal. DOI: 10.3390/diabetology2020004.
Y. L. Tinungki and J. S. H. Hinonaung, "Deteksi Dini Penyakit Diabetes Mellitus (DM) dan Obat Tradisional DM Pada Lansia di Kabupaten Kepulauan Sangihe," PT. Sonpedia Publishing Indonesia, Nov. 22, 2023.
L. Cloete, "Diabetes mellitus: an overview of the types, symptoms, complications and management," Nursing Standard (Royal College of Nursing (Great Britain) : 1987), vol. 37, no. 1, pp. 61-66, Jan. 2022. DOI: 10.7748/ns.2021.e11709. PMID: 34708622.
C.S. Lau and T.C. Aw, "HbA1c in The Diagnosis and Management of Diabetes Mellitus: An Update," Diabetes, vol. 6, pp. 1-4, 2020. DOI: 10.15761/DU.1000137
A. Sonabend-W, J. Lu, L. A. Celi, T. Cai, and P. Szolovits, "Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation," in Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020.
R. S. Sutton and A. G. Barto, "Reinforcement Learning: An Introduction," 2nd ed. MIT Press, Oct. 19, 2018. ISBN: 9780262352703, 0262352702
A. S. Chauhan, M. S. Varre, K. Izuora, M. B. Trabia, and J. S. Dufek, "Prediction of Diabetes Mellitus Progression Using Supervised Machine Learning," Sensors, vol. 23, no. 10, p. 4658, May 11, 2023. DOI: 10.3390/s23104658.
D. Datta, M. Bhattacharya, S. S. Rajest, T. Shynu, R. Regin, and S. S. Priscila, "Development of Predictive Model of Diabetic Using Supervised Machine Learning Classification Algorithm of Ensemble Voting," International Journal of Bioinformatics Research and Applications, vol. 19, no. 3, pp. 151-169, Sep. 28, 2023. DOI: 10.1504/IJBRA.2023.133695.
B. Chen, C. Zhu, P. Agrawal, K. Zhang, and A. Gupta, "Self-Supervised Reinforcement Learning that Transfers using Random Features," presented at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Main Conference, May 26, 2023. DOI: 10.48550/arXiv.2305.17250.
T. Zhou, T. Guo, C. Dang, and M. Beer, "Bayesian reinforcement learning reliability analysis," Computer Methods in Applied Mechanics and Engineering, vol. 424, p. 116902, May 1, 2024. DOI: 10.1016/j.cma.2024.116902. Published by Elsevier.
X. Xin, A. Karatzoglou, I. Arapakis, and J. M. Jose, "Self-Supervised Reinforcement Learning for Recommender Systems," in SIGIR ’20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2020, pp. 931-940, doi: 10.1145/3397271.3401147.
J. C. Lauffenburger, E. Yom-Tov, P. A. Keller, M. E. McDonnell, L. G. Bessette, C. P. Fontanet, E. S. Sears, E. Kim, K. Hanken, J. J. Buckley, R. A. Barlev, N. Haff, and N. K. Choudhry, "REinforcement Learning to Improve Non-Adherence for Diabetes Treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial," BMJ Open, vol. 11, e052091, Nov. 11, 2021. DOI:10.1136/bmjopen-2021-052091.
J. He, H. Zhao, D. Zhou, and Q. Gu, "Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes," in Proceedings of the 40th International Conference on Machine Learning, PMLR 202, pp. 12790-12822, 2023.
M. H. Lim, W. H. Lee, B. Jeon, and S. Kim, "A Blood Glucose Control Framework Based on Reinforcement Learning With Safety and Interpretability: In Silico Validation," IEEE Access, vol. 9, pp. 113334-113347, Jul. 26, 2021. DOI: 10.1109/ACCESS.2021.3100007.
T. Zhu and P. Herrero, "Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation," IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 9, pp. 2168-2194, 2020, doi: 10.1109/JBHI.2020.3014556.
J. Jumiyatun, I. Mahmudi, and A. Mustari, "Kontrol Power Elektronik Dan Aplikasinya: Perancangan, Pemodelan, Simulasi dan Implementasi," Media Nusa Creative (MNC Publishing), Nov. 15, 2021.
Z. Liu, W. Zhao, S. Liu, M. Feng, L. Ji, X. Liao, X. Sun, G. Xie, X. Jiang, T. Zhao, and G. Hu, "A Deep Fment Learning Approach for Type 2 Diabetes Mellitus Treatment," in 2020 IEEE International Conference on Healthcare Informatics (ICHI), 2020, DOI: 10.1109/ICHI48887.2020.9374313.
