Blood Glucose Baseline Determination Using Machine Learning
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Solution Overview
Problem
Current methods for determining blood glucose baselines are inadequate as they do not accurately account for various impact factors, leading to imprecise and unreliable calculations of blood glucose responses to food and other factors, which can result in excessive insulin secretion and insulin resistance, contributing to chronic diseases like diabetes and obesity.
Innovation Solution
A computer-implemented method using machine learning to determine a baseline in a blood glucose curve by training an algorithm with data that includes impact factor-accounting baselines, allowing for precise and reliable calculation of blood glucose responses by considering the nature, duration, and extent of influencing factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional baseline determination methods are used, then the calculation process is simple, but the measurement precision of blood glucose response is poor
Solution Approach 1:
The patent applies preliminary action by pre-training a machine learning algorithm with training data that includes impact factor-accounting baselines. This pre-computed knowledge is then applied to automatically determine baselines in new blood glucose curves, improving precision without requiring complex manual calculations at the time of measurement
Solution Approach 2:
The patent replaces traditional mechanical/mathematical baseline determination methods with a machine learning-based automated system. The trained algorithm automatically identifies baselines by learning from training data that accounts for various impact factors, substituting complex manual calculations with an intelligent automated system
2Reliability
If impact factors are not considered, then the measurement process is fast, but the reliability of blood glucose response determination is poor
Solution Approach 1:
The patent uses preliminary action by pre-processing and incorporating impact factor information into the training data before the actual measurement. The machine learning algorithm learns to account for impact factors during training, so that during actual use, reliable baseline determination occurs automatically without time-consuming manual analysis of impact factors
Solution Approach 2:
The trained machine learning algorithm performs self-service by automatically determining baselines while independently considering impact factors. The system serves itself by having the algorithm autonomously identify and account for relevant impact factors based on patterns learned during training, eliminating the need for manual time investment
3Measurement precision
If individualized blood glucose curves are measured for every condition, then the measurement precision is high, but the productivity of obtaining recommendations is low
Solution Approach 1:
The patent applies universality by creating a single trained machine learning algorithm that serves multiple functions: it determines baselines, accounts for various impact factors, and enables personalized recommendations across different conditions. This universal algorithm replaces the need for separate individualized measurements for every scenario
Solution Approach 2:
The patent uses copying by creating a trained algorithm model from training data that represents individualized responses. This copied knowledge is then applied to new situations without requiring actual individualized measurements each time, maintaining precision while improving productivity through model reuse
Data Source
AI summary
The present invention pertains to a method for determining a baseline in a blood glucose curve, a method for determining a blood glucose response of an individual to at least one impact factor, a method for predicting the nutritype of an individual, a method for predicting the blood glucose response of an individual to at least one impact factor, a method for determining personalized lifestyle recommendations for an individual as well as a method for determining the composition of a personalized diet and a method for preparation thereof.