This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1915) Diabetes Health Services and System Innovations (141)

Published on in Vol 23, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27858, first published .
Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation

Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation

Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation

Authors of this article:

Xingzhi Sun1 Author Orcid Image ;   Yong Mong Bee2, 3 Author Orcid Image ;   Shao Wei Lam4, 5 Author Orcid Image ;   Zhuo Liu1 Author Orcid Image ;   Wei Zhao1 Author Orcid Image ;   Sing Yi Chia6 Author Orcid Image ;   Hanis Abdul Kadir5, 6 Author Orcid Image ;   Jun Tian Wu4 Author Orcid Image ;   Boon Yew Ang4 Author Orcid Image ;   Nan Liu4, 5 Author Orcid Image ;   Zuo Lei1 Author Orcid Image ;   Zhuoyang Xu1 Author Orcid Image ;   Tingting Zhao1 Author Orcid Image ;   Gang Hu1 Author Orcid Image ;   Guotong Xie1, 7, 8 Author Orcid Image
Article Authors Cited by (17) Tweetations (6) Metrics

Journals

  1. Di S, Petch J, Gerstein H, Zhu R, Sherifali D. Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study. JMIR Formative Research 2022;6(9):e37838 View
  2. Oh S, Park J, Lee S, Kang S, Mo J. Reinforcement learning-based expanded personalized diabetes treatment recommendation using South Korean electronic health records. Expert Systems with Applications 2022;206:117932 View
  3. Oh S, Lee S, Park J. Effective data-driven precision medicine by cluster-applied deep reinforcement learning. Knowledge-Based Systems 2022;256:109877 View
  4. Woodman R, Mangoni A. A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future. Aging Clinical and Experimental Research 2023;35(11):2363 View
  5. Woodman R, Koczwara B, Mangoni A. Applying precision medicine principles to the management of multimorbidity: the utility of comorbidity networks, graph machine learning, and knowledge graphs. Frontiers in Medicine 2024;10 View
  6. Xu Z, Gu Y, Xu X, Topaz M, Guo Z, Kang H, Sun L, Li J. Developing a Personalized Meal Recommendation System for Chinese Older Adults: Observational Cohort Study. JMIR Formative Research 2024;8:e52170 View
  7. Yoon S, Goh H, Lee P, Tan H, Teh M, Lim D, Kwee A, Suresh C, Carmody D, Swee D, Tan S, Wong A, Choo C, Wee Z, Bee Y. Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence–Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study. JMIR Human Factors 2024;11:e50939 View
  8. Zhao Y, Chaw J, Liu L, Chaw S, Ang M, Ting T. Systematic literature review on reinforcement learning in non-communicable disease interventions. Artificial Intelligence in Medicine 2024;154:102901 View
  9. Nambiar M, Bee Y, Chan Y, Ho Mien I, Guretno F, Carmody D, Lee P, Chia S, Salim N, Krishnaswamy P. A drug mix and dose decision algorithm for individualized type 2 diabetes management. npj Digital Medicine 2024;7(1) View
  10. Wong W, Nguyen T, Ahmad F, Vu H, Koh A, Tan K, Zhang Y, Harrison C, Woodward M, Nguyen T. Hypertension in Adults With Diabetes in Southeast Asia: A Systematic Review. The Journal of Clinical Hypertension 2025;27(1) View
  11. Garg S, Kitchen R, Gupta R, Pearson E. Applications of AI in Predicting Drug Responses for Type 2 Diabetes. JMIR Diabetes 2025;10:e66831 View
  12. Brož J. Treatment of type 2 diabetes mellitus - a current view of the different drug classes and strategies for their use. Vnitřní lékařství 2025;71(3):144 View
  13. Sarani Rad F, Li J. Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework. JMIR Diabetes 2025;10:e72874 View
  14. Wang M, Sushil M, Williams C, Miao B, Kim S, Masharani U, Ku G, Hsiao V, Shah A, Koliwad S, Butte A. Assessment of Large Language Models for Enhancing Diabetologist-Developed Personalized Treatment Plans in Complex Type 2 Diabetes. Clinical Diabetes 2025;43(4):545 View
  15. Andarge B, Negash N, Degualem S, Bezie H, Wondmagegn H, Meshesha T, Gembe M, Habtegiorgis Y. Factors associated with hypertension among patients with type 2 diabetes mellitus at Arba Minch general hospital, South Ethiopia. Preventive Medicine Reports 2025;59:103277 View

Conference Proceedings

  1. Nambiar M, Ghosh S, Ong P, Chan Y, Bee Y, Krishnaswamy P. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications View
  2. Sharma M, Kumar P. 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence). A Novel Q-Learning Frameworkfor Type-II Diabetes Prediction View