Adaptive Insulin Controller Gain for Patient-Specific Sensitivity
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Solution Overview
Problem
Current methods for controlling blood glucose levels in critically ill patients are unreliable and lack clinically tested protocols, leading to increased risk of hyperglycemia and complications such as prolonged hospital stays or death.
Innovation Solution
A method using a mathematical model to determine patient-specific insulin sensitivity, taking into account measured glucose levels, actual insulin infusion rates, and glucose intake, which adjusts the controller gain to compute a recommended infusion rate, ensuring accurate glucose level management and adaptability to changing patient conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed controller gain is used to compute the recommended infusion rate, then the control method is simple to implement, but it cannot adapt to changing patient conditions and insulin sensitivity variations
Solution Approach 1:
The controller gain is made dynamic by continuously adapting it based on the determined patient-specific insulin sensitivity. The system transitions from a static fixed gain approach to a dynamic adaptive gain approach, where the controller gain changes in response to varying patient conditions and insulin sensitivity levels, enabling the control system to adapt to changing physiological states.
Solution Approach 2:
The system automatically determines patient-specific insulin sensitivity using a mathematical model that processes measured glucose levels, actual infusion rates, and glucose intake data. This self-determination eliminates the need for manual sensitivity assessment and enables automatic adaptation of the controller gain without requiring external intervention or complex manual calibration procedures.
2Measurement precision
If a patient-specific insulin sensitivity determination is implemented, then the control accuracy is improved, but the computational requirements increase
Solution Approach 1:
The complex computational task of determining insulin sensitivity is extracted from the real-time control loop and performed using a dedicated mathematical model that processes available measurement data. This extraction allows the sensitivity determination to be computed efficiently using existing glucose level, infusion rate, and glucose intake data without requiring additional complex real-time calculations during the control cycle.
Solution Approach 2:
The mathematical model continuously determines patient-specific insulin sensitivity in advance of the control decision, using available measurement data to pre-compute the sensitivity value. This preliminary determination of insulin sensitivity allows the controller to use an accurate, patient-specific gain value without performing complex calculations at the moment of control action, reducing real-time computational burden.
3Reliability
If the controller gain is continuously adapted based on insulin sensitivity, then the reliability of glucose control is improved, but the system complexity increases
Solution Approach 1:
A single mathematical model serves multiple functions: it processes measured glucose levels, actual infusion rates, and glucose intake data to determine both the patient-specific insulin sensitivity and provides the basis for controller gain adaptation. This multi-functional approach consolidates what could be separate complex subsystems into one unified model, improving reliability while managing system complexity.
Solution Approach 2:
The system implements continuous feedback by using the determined patient-specific insulin sensitivity to adapt the controller gain, which in turn affects the recommended infusion rate that is applied to the patient. This closed-loop feedback mechanism ensures that the control system continuously adjusts to the patient's actual physiological state, improving reliability through automatic correction based on real-time sensitivity assessment.
Data Source
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AI summary
In a method for controlling the administration of insulin to a patient (P) a target glucose level (T) is set and a controller (10) computes a recommended infusion rate (R) based on the target glucose level (T) and a measured glucose level (G) of a patient (P) for administering insulin to the patient (P). The controller (10) comprises a controller gain (K) for computing the recommended infusion rate (R). Herein, an insulin sensitivity (IS) of the patient (P) is determined by means of a mathematical model taking into account the measured glucose level (G) and an actual infusion rate (IR) of insulin administered to the patient (P) and, based on the insulin sensitivity (IS), the controller gain (K) of the controller (10) is determined. In this way a method for controlling the administration of insulin to a patient is provided which in a reliable, computationally efficient manner allows for maintaining a patient's blood glucose level at or around a desired target glucose level.