Predicted Time to Assess Glycemic State Control
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Solution Overview
Problem
Current glucose monitoring and control systems for diabetic patients, particularly those using infusion pumps, face challenges in accurately predicting blood glucose levels and responding to deviations, leading to risks of hypoglycemia and hyperglycemia, especially when patients are not fully attentive to their glycemic management.
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 risks of glycemic extremes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a closed-loop infusion pump system is used to automatically control insulin infusion based on real-time blood glucose measurements, then glycemic control is improved and the risk of hypoglycemia and hyperglycemia is reduced, but the device complexity and cost increase
Solution Approach 1:
The patent combines multiple functions (glucose sensing, insulin storage, infusion pumping, and control processing) into an integrated closed-loop system. The glucose sensor, infusion pump, and controller are merged into a single cohesive device that automatically regulates blood glucose levels, eliminating the need for separate monitoring and administration systems.
Solution Approach 2:
The system implements self-service through automatic control algorithms that continuously monitor blood glucose levels and adjust insulin infusion rates without requiring patient intervention. The controller autonomously processes sensor data, predicts future glucose levels, and generates appropriate insulin delivery commands, reducing the burden on patients to manually manage their diabetes.
2Measurement precision
If continuous blood glucose monitoring is implemented to enable real-time control, then the accuracy of glycemic management is improved, but the loss of time for system response and the complexity of continuous measurement increase
Solution Approach 1:
The system performs preliminary action by using the controller to predict future blood glucose levels based on current and historical sensor data. This predictive capability allows the system to prepare insulin delivery commands in advance, anticipating glucose level changes before they occur and reducing the effective response time to glycemic deviations.
Solution Approach 2:
The closed-loop system implements continuous feedback by constantly monitoring blood glucose levels through the sensor and using this information to adjust insulin infusion rates. The controller receives real-time sensor signals, processes them through control algorithms, and automatically modifies pump operation to maintain glycemic targets, creating a dynamic responsive system.
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.