Insulin Dosage Optimization Using Feedback and Segmentation
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Solution Overview
Problem
Conventional methods for managing diabetes lack accuracy in calculating insulin dosages, as they do not adequately consider a patient's past glycemic history, meal type, time period, and physical condition, leading to suboptimal glycemic control and potential hypoglycemia or hyperglycemia.
Innovation Solution
A system and method that utilize a processor to calculate optimized insulin dosages based on current blood glucose readings, meal type, time period, and past glycemic history, incorporating factors like carbohydrate intake and insulin sensitivity to provide personalized insulin recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trial and error methods are used to estimate insulin dosage, then the system is simple and easy to operate, but the accuracy of blood glucose control deteriorates leading to hypoglycemia or hyperglycemia
Solution Approach 1:
The system continuously monitors blood glucose levels and uses this feedback to dynamically adjust insulin dosage recommendations. The processor analyzes current blood glucose readings along with historical data to optimize future dosing decisions, creating a closed-loop control system that improves accuracy while maintaining ease of use through automated calculations.
Solution Approach 2:
The system performs preliminary analysis of multiple factors including past glycemic history, meal type, time period, and physical condition before recommending insulin dosage. By pre-processing these interdependent parameters and storing them in databases, the system prepares optimized dosage recommendations in advance, reducing the cognitive burden on patients and improving dosing accuracy.
2Reliability
If multiple interdependent parameters are considered for insulin dosage optimization, then the accuracy of blood glucose control improves, but the complexity of data processing and system operation increases
Solution Approach 1:
The system automatically collects and processes data without requiring manual intervention for complex calculations. The processor autonomously analyzes blood glucose readings, meal types, time periods, and physical conditions to generate insulin dosage recommendations. This self-service approach maintains high reliability through comprehensive parameter analysis while preserving ease of operation by presenting simple recommendations to the user.
Solution Approach 2:
The system segments the complex dosage calculation into distinct modular components: blood glucose monitoring module, meal type identification module, time period analysis module, and dosage recommendation module. Each module processes specific parameters independently before integrating results, making the system easier to operate while maintaining comprehensive analysis for reliable glycemic control.
3Measurement precision
If past glycemic history is incorporated into dosage calculations, then the precision of insulin recommendations improves, but the amount of data storage and processing requirements increases
Solution Approach 1:
The system extracts only the most relevant features from past glycemic history data for dosage calculations, rather than processing entire historical datasets. The processor identifies and utilizes key patterns such as response to specific meal types at different time periods, while storing compressed representations of historical data. This extraction approach improves dosage precision through historical learning while minimizing data storage requirements.
Data Source
AI summary
A method and system for optimizing insulin dosages for diabetic subjects which includes a processor for calculating basal and bolus dosages to be recommended for meal types including breakfast, lunch, dinner, snack, or at miscellaneous times. The bolus calculations are specifically directed to time periods which are taken from either pre-meal, post-meal, bedtime, mid-sleep or miscellaneous times. The processor calculates an optimized bolus for a specific time period and meal type based upon prior basal dosages, prior glucose doses, hypoglycemia thresholds, mid-point of target ranges, and subject insulin sensitivity factors. A display is provided to the subject for sensing the optimized insulin dosage recommended at a specific time period and for a specific meal type.