ArticleJMIR diabetes2019
A Reinforcement Learning-Based Method for Management of Type 1 Diabetes: Exploratory Study.
Article in JMIR diabetes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Personalized Type 1 Diabetes Management: Reinforcement Learning-Based Insulin Dosing and Glucose Forecasting.JMIR diabetes · 2026Article
- Personalized Glucose Management With AI: Pilot Study Using a Multiarmed Bandit Approach.JMIR formative research · 2026Article
- Artificial Intelligence in Personalized Medicine for Diabetes Mellitus: A Narrative Review.Cureus · 2025Review
- Artificial intelligence in endocrinology: a comprehensive review.Journal of endocrinological investigation · 2024Review
- Applying Reinforcement Learning for Enhanced Cybersecurity against Adversarial Simulation.Sensors (Basel, Switzerland) · 2023Article
- Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review.Diabetology & metabolic syndrome · 2022Review
- 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 : JAMIA · 2022Article
- Precision Medicine for Hypertension Patients with Type 2 Diabetes via Reinforcement Learning.Journal of personalized medicine · 2022Article
- Probabilistic Machine Learning for Healthcare.Annual review of biomedical data science · 2021Review
- Machine Learning Applications in Endocrinology and Metabolism Research: An Overview.Endocrinology and metabolism (Seoul, Korea) · 2020Review
- RNA-based diagnostic innovations: A new frontier in diabetes diagnosis and management.Diabetes & vascular disease researchReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin therapy to regulate blood glucose levels and prevent the long-term complications of the disease. Currently, there are no effective algorithms that consider the unique characteristics of T1DM patients to automatically recommend personalized insulin dosage levels.
objectiveThe objective of this study was to develop and validate a general reinforcement learning (RL) framework for the personalized treatment of T1DM using clinical data.
methodsThis research presents a model-free data-driven RL algorithm, namely Q-learning, that recommends insulin doses to regulate the blood glucose level of a T1DM patient, considering his or her state defined by glycated hemoglobin (HbA
resultsA total of 87 patients were included in the training set. The mean age of these patients was 53 years, 59% (51/87) were male, 86% (75/87) were white, and 47% (41/87) were married. The performance of the RL agent was evaluated on 60 test cases. RL agent-recommended insulin dosage interval includes the actual dose prescribed by the physician in 53 out of 60 cases (53/60, 88%).
conclusionsThis exploratory study demonstrates that an RL algorithm can be used to recommend personalized insulin doses to achieve adequate glycemic control in patients with T1DM. However, further investigation in a larger sample of patients is needed to confirm these findings.
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