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

Glucose Tracking and Self-Monitoring of Blood Glucose (166) Machine Learning (1501)

Published on in Vol 4, No 3 (2019): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12905, first published .
A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study

A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study

A Reinforcement Learning–Based Method for Management of Type 1 Diabetes: Exploratory Study

Authors of this article:

Mahsa Oroojeni Mohammad Javad1 Author Orcid Image ;   Stephen Olusegun Agboola2, 3 Author Orcid Image ;   Kamal Jethwani2 Author Orcid Image ;   Abe Zeid4 Author Orcid Image ;   Sagar Kamarthi4 Author Orcid Image
Article Authors Cited by (18) Tweetations (3) Metrics

Journals

  1. Hong N, Park H, Rhee Y. Machine Learning Applications in Endocrinology and Metabolism Research: An Overview. Endocrinology and Metabolism 2020;35(1):71 View
  2. Hong N, Park H, Rhee Y. Machine Learning Application in Diabetes and Endocrine Disorders. The Journal of Korean Diabetes 2020;21(3):130 View
  3. Chen I, Joshi S, Ghassemi M, Ranganath R. Probabilistic Machine Learning for Healthcare. Annual Review of Biomedical Data Science 2021;4(1):393 View
  4. 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
  5. Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi M. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetology & Metabolic Syndrome 2022;14(1) View
  6. Martyshina A, Tilinova O, Simanova A, Knyazeva O, Dokukina I. The maze runner: navigating through basic kinetics to AI models of human metabolism pathology. Procedia Computer Science 2022;213:271 View
  7. Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C. Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys 2022;16(none) View
  8. Serafini M, Rosales N, Garelli F. Long-Term Adaptation of Closed-Loop Glucose Regulation Via Reinforcement Learning Tools. IFAC-PapersOnLine 2022;55(7):649 View
  9. Oh S, Lee S, Park J. Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning. Journal of Personalized Medicine 2022;12(1):87 View
  10. Zeng J, Shao J, Lin S, Zhang H, Su X, Lian X, Zhao Y, Ji X, Zheng Z. Optimizing the dynamic treatment regime of in-hospital warfarin anticoagulation in patients after surgical valve replacement using reinforcement learning. Journal of the American Medical Informatics Association 2022;29(10):1722 View
  11. Oh S, Jeong M, Kim H, Park J. Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation. Sensors 2023;23(6):3000 View
  12. Landers M, Doryab A. Deep Reinforcement Learning Verification: A Survey. ACM Computing Surveys 2023;55(14s):1 View
  13. Giorgini F, Di Dalmazi G, Diciotti S. Artificial intelligence in endocrinology: a comprehensive review. Journal of Endocrinological Investigation 2023;47(5):1067 View

Books/Policy Documents

  1. Brown E, Hannah-Shmouni F, Shekhar S. Artificial Intelligence in Clinical Practice. View
  2. Priyadarshini A, Yogesh . Internet of Things and Machine Learning for Type I and Type II Diabetes. View