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Reinforcement Learning: A Paradigm Shift in Personalized Blood Glucose Management for Diabetes
Lehel Dénes-Fazakas1,2,3, László Szilágyi1,2,4, Levente Kovács1,2
1Physiological Controls Research Center, University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.
Biomedicines
|September 28, 2024
Summary
Reinforcement learning improves diabetes management by creating personalized blood glucose control plans. This adaptive approach enhances Time in Range (TIR) for individuals with diabetes.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Endocrinology
Background:
- Effective blood glucose management is crucial for individuals with diabetes.
- Traditional diabetes management lacks personalized flexibility.
- Reinforcement learning (RL) offers adaptive strategies for dynamic patient care.
Purpose of the Study:
- To explore RL for personalized blood glucose management.
- To develop dynamic, adaptive diabetes care plans.
- To improve Time in Range (TIR) in patients with diabetes.
Main Methods:
- Developed a mathematical model for type IVP diabetes patients.
- Incorporated continuous glucose monitoring (CGM) noise and random carbohydrate intake.
- Designed a closed-loop system for RL algorithm implementation, including Policy Optimization (PPO).
Main Results:
- Achieved an average Time in Range (TIR) of 73%.
- Demonstrated improved blood glucose control through RL.
- Validated the model with data from 10 patients and 17 parameters.
Conclusions:
- Presents a personalized insulin therapy solution using RL.
- The closed-loop model shows promise for improved blood glucose regulation.
- Highlights potential applications in personalized diabetes management.
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