Personalized Insulin Delivery Control With Glucose-Based Basal Adjustment
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Solution Overview
Problem
Existing insulin delivery systems fail to account for individual variations in insulin sensitivity and blood glucose levels, leading to potential risks of diabetic ketoacidosis or insufficient insulin delivery due to inaccurate glucose data and personalization algorithms.
Innovation Solution
Implementing a control device that personalizes insulin delivery by adjusting basal rates based on real-time glucose data, using constraints and thresholds to prevent excessive or insufficient insulin delivery, and issuing alarms or alerts when thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If insulin delivery is personalized based on real-time glucose data, then insulin delivery effectiveness is improved, but risk of excessive or insufficient insulin delivery increases
Solution Approach 1:
The system continuously monitors glucose levels and adjusts insulin delivery based on real-time feedback from glucose readings. The control device receives glucose data, compares it to target ranges, and modifies basal rate deliveries accordingly, creating a closed-loop control system that adapts to changing physiological conditions while maintaining safety through continuous monitoring
Solution Approach 2:
The system dynamically changes insulin delivery parameters (basal rate, bolus amounts) based on glucose level parameters. When glucose levels fall outside acceptable ranges, the system adjusts delivery parameters to bring levels back into target zones, allowing flexible adaptation to individual patient needs while preventing dangerous deviations
2Adaptability or versatility
If personalization algorithms adjust basal rates dynamically, then response to individual variations is improved, but risk of inaccurate glucose data effects increases
Solution Approach 1:
The system performs preliminary validation of glucose data before adjusting insulin delivery. It checks for data quality, compares readings against expected patterns, and verifies consistency before triggering basal rate adjustments. This preliminary action prevents inaccurate or erroneous glucose data from causing inappropriate insulin delivery changes
Solution Approach 2:
The control device acts as an intermediary between glucose monitoring and insulin delivery functions. It processes glucose data through multiple validation layers, applies safety constraints, and mediates the translation into insulin delivery commands. This intermediary role filters out errors and ensures that only validated, reliable data drives delivery adjustments
3Reliability
If constraints and thresholds are implemented to prevent excessive insulin delivery, then safety is improved, but system complexity increases
Solution Approach 1:
The safety constraint system is segmented into distinct functional modules: glucose data validation, threshold comparison, constraint checking, and delivery adjustment. Each module handles a specific aspect of safety, making the overall complex system manageable through modular design where each segment can be independently tested and maintained
Data Source
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AI summary
An insulin delivery monitoring system that includes an insulin delivery device and a controller configured to perform or control performance of operations. A method of insulin delivery include obtaining one or more blood glucose readings of a user, and, based on the blood glucose readings, generating a set of insulin delivery actions that may include delivery of a baseline basal rate or predefined variations of the baseline basal rate.