Linear Regression Insulin Dose Prediction Model
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Solution Overview
Problem
Current methods for predicting glucose levels and determining optimal insulin doses in diabetic patients are not individualized and fail to account for external factors like dietary carbohydrates and exercise, relying on universal predictions and not utilizing the full potential of continuous glucose monitor data.
Innovation Solution
A linear regression-based method that learns each patient's unique response to short-acting and long-acting insulin, dietary carbohydrates, and lifestyle factors like exercise, calculating time-dependent response curves to determine optimal insulin dosages and ratios, with an asymmetrical cost function to prioritize avoiding hypoglycemia.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If universal prediction models are used for glucose levels, then the model complexity is reduced and can be applied broadly, but the prediction accuracy and individualization for each patient deteriorates
Solution Approach 1:
The patent segments the prediction model into two parts: a universal structural framework that can be applied to all patients, and patient-specific parameters (insulin sensitivity factor, carbohydrate ratio, exercise factor) that are individually calibrated. This segmentation allows the model to maintain broad applicability while achieving individualized accuracy through personalized parameter estimation.
2Measurement precision
If external factors like dietary carbohydrates and exercise are incorporated into the prediction model, then the accuracy of glucose prediction improves, but the complexity of the model increases
Solution Approach 1:
The patent incorporates external factors by introducing specific parameters for each factor (carbohydrate ratio for dietary intake, exercise factor for physical activity, insulin sensitivity factor for insulin response). These parameters modify the base prediction model in a systematic way, allowing accurate accounting of multiple influences while maintaining a relatively simple additive model structure that is easy to implement and interpret.
3Reliability
If continuous glucose monitor data is fully utilized for determining optimal insulin doses, then the therapeutic effectiveness improves, but the difficulty of data processing and analysis increases
Solution Approach 1:
The patent implements a feedback mechanism where continuous glucose monitor readings are fed into the prediction model, which then generates insulin dosage recommendations. The model uses the observed glucose responses to refine parameter estimates over time, creating a closed-loop system that continuously learns from actual patient data. This feedback approach transforms raw data into actionable therapeutic guidance while adapting to individual patient responses.
Data Source
AI summary
This invention relates to a method and a device for predicting the glucose concentration of a subject and recommending therapeutic action. The responses of the user's glucose to administered doses of insulin, dietary carbohydrates, and other factors influencing glucose concentration are measured individually for a given user. Once these responses are learned as a function of time, the method and device can receive information about the factors that have been recently or will soon be administered and can recommend which other factors should also be administered.