Regression Model for TDBD Mismatch in Insulin Therapy
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Solution Overview
Problem
Current insulin therapy management systems struggle to accurately determine and adjust the total daily basal dose (TDBD) mismatch, leading to suboptimal glycemic control and prolonged convergence time to ideal blood glucose ranges, which can negatively impact health and quality of life for individuals with diabetes.
Innovation Solution
A method and system that utilize a regression model trained on data, including blood glucose values and time-of-administration information, to determine a TDBD mismatch value, allowing for dynamic adjustments to insulin therapy settings, such as the carbohydrate-to-insulin ratio and insulin sensitivity factor, to compensate for the mismatch, thereby improving insulin delivery precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current insulin therapy management systems are used to determine TDBD mismatch, then the system structure is simple, but the measurement precision of TDBD mismatch is insufficient
Solution Approach 1:
A regression model is introduced as an intermediary computational layer between the input data (blood glucose values and time-of-administration information) and the output (TDBD mismatch value). This model processes the relationship between insulin administration timing and blood glucose responses to accurately determine the TDBD mismatch, resolving the contradiction by adding intelligent processing capability without requiring complete redesign of the entire system architecture.
Solution Approach 2:
The system dynamically adjusts insulin therapy parameters (carbohydrate-to-insulin ratio and insulin sensitivity factor) based on the determined TDBD mismatch value. By changing these parameters adaptively, the system achieves precise glycemic control while maintaining a relatively simple operational framework, thus improving measurement precision without proportionally increasing device complexity.
2Productivity
If manual adjustment of insulin therapy settings is used, then the device complexity is low, but the convergence speed to optimal blood glucose range is slow
Solution Approach 1:
The system implements a feedback mechanism where blood glucose values and time-of-administration information are continuously monitored, processed through a regression model to determine TDBD mismatch, and used to automatically adjust insulin therapy settings. This closed-loop feedback system accelerates convergence to optimal glycemic control by continuously learning from and responding to patient data, while maintaining manageable complexity through automated calculation rather than complex mechanical adjustment mechanisms.
Solution Approach 2:
The regression model enables the system to automatically determine TDBD mismatch and adjust insulin therapy parameters without requiring manual intervention. The system serves itself by processing its own operational data (blood glucose values and administration timing) to generate optimized therapy settings, thereby increasing productivity while keeping the user interface simple and the overall device complexity moderate.
3Reliability
If inaccurate TDBD mismatch determination is used, then the system is easier to operate, but the glycemic control quality is poor
Solution Approach 1:
The system performs preliminary analysis by training a regression model on historical data before actual TDBD mismatch determination. This pre-computed model captures the complex relationships between insulin administration patterns and blood glucose responses, enabling accurate real-time mismatch determination without requiring complex manual calculations or adjustments during operation. The reliability of glycemic control is improved through this preparatory modeling step, while ease of operation is maintained because the actual user interaction remains simple.
Data Source
AI summary
A method comprises: receiving, in a computer system, data regarding insulin therapy treatment of a person with diabetes, the data relating to a time period and comprising blood glucose values for the person with diabetes and time-of-administration information for the insulin therapy treatment; determining, using the computer system and based on the blood glucose values and the time-of-administration information, a total daily basal dose (TDBD) mismatch value for the person with diabetes; and generating, using the computer system, an output based on the determined TDBD mismatch value.