Glucose Pattern Discovery Using Similarity Clustering
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Solution Overview
Problem
Individuals with diabetes face challenges in maintaining healthy blood glucose levels, and existing glucose monitoring technologies lack effective methods to visualize and analyze glucose patterns to guide lifestyle changes for improved glycemic control.
Innovation Solution
A diabetes management platform that applies similarity algorithms and clustering techniques to glucose monitoring data, enabling the identification of patterns and visualization of blood glucose levels to provide personalized feedback for healthier glycemic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional glucose monitoring methods are used, then glucose levels can be measured, but the data cannot be effectively visualized or analyzed to guide lifestyle changes
Solution Approach 1:
The patent introduces an intermediary system comprising a processor that applies similarity algorithms and clustering techniques to the raw glucose monitoring data. This intermediary processing layer transforms complex, difficult-to-interpret glucose data into meaningful patterns and visualizations that guide lifestyle changes, resolving the contradiction between preserving information and enabling ease of analysis.
Solution Approach 2:
The patent replaces traditional manual or simple mechanical analysis methods with advanced computational algorithms including similarity measurements and clustering techniques. This substitution enables automatic detection of glucose patterns and generation of actionable insights, transforming the data analysis capability from a manual process to an automated intelligent system.
2Measurement precision
If similarity algorithms and clustering techniques are applied to glucose data, then patterns can be detected and visualized, but the system complexity increases
Solution Approach 1:
The patent segments the complex data processing task into distinct functional modules: a similarity algorithm module that compares glucose data sets, a clustering technique module that groups similar patterns, and a visualization module that presents results. This segmentation manages system complexity by organizing the processing pipeline into manageable, specialized components while maintaining high pattern detection accuracy.
Solution Approach 2:
The patent creates a universal diabetes management platform that performs multiple functions: monitoring glucose levels, applying similarity algorithms, executing clustering techniques, generating visualizations, and providing lifestyle recommendations. This multi-functional system consolidates various processing tasks into a single integrated platform, managing complexity through functional consolidation rather than separate systems.
Data Source
AI summary
Introduced here are diabetes management platforms able to examine glucose measurements associated with a subject to discover patterns indicative of similar behaviors/circumstances. More specifically, a diabetes management platform can acquire multiple data series generated by a glucose monitoring device that monitors the blood glucose level of an individual over multiple time intervals. The diabetes management platform can then apply a similarity algorithm to produce a distance measure (also referred to as a “similarity measure”) for each data series. For example, the diabetes management platform may, for each data series, perform dynamic time warping to produce a distance measure with respect to each of the other data series. The diabetes management platform can identify patterns indicative of similar behaviors/circumstances based on these distance measures.