Deep RL for Blood Glucose Control: Lessons, Challenges, and Opportunities
Individuals with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-administer in order to adequately control their blood glucose levels. Longitudinal data streams captured from wearables, like continuous glucose monitors, can help these individuals manage their health, but currently the majority of the decision burden remains on the user. To relieve this burden, researchers are working on closed-loop solutions that combine a continuous glucose monitor and an insulin pump with a control algorithm in an `artificial pancreas.' Such systems aim to estimate and deliver the appropriate amount of insulin. Here, we develop reinforcement learning (RL) techniques for automated blood glucose control. Through a series of experiments, we compare the performance of different deep RL approaches to non-RL approaches. We highlight the flexibility of RL approaches, demonstrating how they can adapt to new individuals with little additional data. On over 21k hours of simulated data across 30 patients, RL approaches outperform baseline control algorithms (increasing time spent in normal glucose range from 71% to 75%) without requiring meal announcements. Moreover, these approaches are adept at leveraging latent behavioral patterns (increasing time in range from 58% to 70%). This work demonstrates the potential of deep RL for controlling complex physiological systems with minimal expert knowledge.
Code (0)
등록된 구현이 없습니다.
Tasks
Reinforcement Learning (RL)Similar Papers 제목 키워드 기반
Using Contextual Information to Improve Blood Glucose Prediction
Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the infl…
Gaussian ProcessesManagementPredictionValue predictionOffline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes
The widespread adoption of effective hybrid closed loop systems would represent an important milestone of care for people living with type 1 diabetes (T1D). These devices typically utilise simple control algorithms to se…
Offline RLReinforcement Learning (RL)Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting
The availability of continuous glucose monitors as over-the-counter commodities have created a unique opportunity to monitor a person's blood glucose levels, forecast blood glucose trajectories and provide automated inte…
Knowledge DistillationComputational Drug Repositioning Using Continuous Self-controlled Case Series
Computational Drug Repositioning (CDR) is the task of discovering potential new indications for existing drugs by mining large-scale heterogeneous drug-related data sources. Leveraging the patient-level temporal ordering…
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control
People with type 1 diabetes (T1D) lack the ability to produce the insulin their bodies need. As a result, they must continually make decisions about how much insulin to self-administer to adequately control their blood g…
Deep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)