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

Glucose Tracking and Self-Monitoring of Blood Glucose (184) Machine Learning (1745)

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 (20) 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
  14. Alum E, Ikpozu E, Offor C, Igwenyi I, Obaroh I, Ibiam U, Ukaidi C. RNA-based diagnostic innovations: A new frontier in diabetes diagnosis and management. Diabetes & Vascular Disease Research 2025;22(2) 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

Conference Proceedings

  1. Roy S, Bhateja G, Gulati G, Saxena S. 2022 Seventh International Conference on Parallel, Distributed and Grid Computing (PDGC). Physiological Parameter Analysis for Type-1 Diabetes and ML Approach for Insulin Prediction View
  2. Wang Z, Xie Z, Tu E, Zhong A, Liu Y, Ding J, Yang J. 2021 International Joint Conference on Neural Networks (IJCNN). Reinforcement Learning-Based Insulin Injection Time And Dosages Optimization View
  3. Serafini M, Fushimi E, Garelli F. 2024 IEEE Biennial Congress of Argentina (ARGENCON). Reinforcement Learning Adjustment of Conventional Insulin Therapy for People with Type 1 Diabetes View
  4. Wambura Y, Moharana T, Dash S, Abdullah A. 2024 6th International Conference on Computational Intelligence and Networks (CINE). Enhancing Insulin Dosage Prediction for Diabetes Management Using Multiple-linear Regression with PCA View