Closed-loop glucose control via predicted trajectory
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Solution Overview
Problem
Current insulin delivery systems for diabetes management, such as infusion pumps, lack advanced predictive capabilities to effectively maintain blood glucose levels within target ranges, particularly in response to changing patient conditions and external factors, leading to risks of hypoglycemia and hyperglycemia.
Innovation Solution
A closed-loop glucose control system that includes a glucose sensor, a controller, and an insulin infusion pump, which predicts the duration for blood glucose to reach a target level based on current observations and generates commands to adjust insulin infusion rates, using techniques like PID algorithms and cost expressions to minimize glycemic deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional insulin delivery systems are used, then the system structure is simple, but the glycemic control precision is insufficient and cannot effectively predict blood glucose trajectories
Solution Approach 1:
The system performs preliminary prediction of blood glucose trajectories using PID algorithms and cost expressions before actual glycemic events occur. By calculating predicted blood glucose levels and time-to-target metrics in advance, the system can proactively adjust insulin delivery to prevent hypoglycemia and hyperglycemia, thereby improving glycemic control precision without requiring complex real-time intervention mechanisms
Solution Approach 2:
The closed-loop system continuously monitors blood glucose levels via glucose sensors and feeds this information back to the controller. The controller uses this feedback to update predictions and adjust insulin pump commands dynamically, creating a self-correcting system that improves precision while managing complexity through automated feedback loops rather than manual intervention
2Reliability
If closed-loop infusion pump systems with real-time glucose sensing are implemented, then glycemic control is improved, but the device complexity and cost increase
Solution Approach 1:
The controller serves multiple functions: it processes glucose sensor data, runs PID prediction algorithms, calculates cost expressions, generates insulin pump commands, and displays information. By consolidating these diverse functions into a single multi-functional controller, the system improves reliability through integrated closed-loop control while managing complexity through functional consolidation rather than separate dedicated components for each task
Solution Approach 2:
The system is designed to autonomously monitor blood glucose levels, predict trajectories, determine appropriate insulin dosing, and adjust pump delivery without requiring continuous user intervention. This self-service capability improves reliability by maintaining consistent closed-loop control while reducing the operational complexity burden on users, requiring only initial setup and monitoring
3Reliability
If predictive algorithms and cost expressions are added to control insulin delivery, then the ability to prevent extreme glycemic events is improved, but the computational complexity increases
Solution Approach 1:
The system uses predictive algorithms to calculate future blood glucose levels and time-to-target metrics before glycemic excursions occur. By performing these computations in advance and using cost expressions to evaluate potential outcomes, the system can select control actions that prevent extreme events while keeping computational complexity manageable through structured prediction models rather than exhaustive simulation
Data Source
AI summary
Presented here are techniques for controlling glucose levels of a patient based on predicted time to a target glucose level. One methodology predicts a trajectory of the blood glucose level based on past observations of the blood glucose level, determines a cost expression based on the trajectory, and affects a future command to an infusion pump to affect a cost value according to the cost expression. Another methodology defines a target blood glucose concentration level for the patient, observes a current blood glucose concentration for the patient based on signals received from a blood-glucose sensor, and predicts a duration of time for the patient's blood glucose concentration to reach the target blood glucose concentration level based on the observed current blood glucose concentration.