Glucose Pattern Analysis for Hypoglycemia Cause Classification
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Solution Overview
Problem
The vast amount of data generated by continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) in diabetes patients is difficult to handle, leading to underutilization of the data, and there is a need for automated analysis and interpretation to identify hypoglycemic events and provide treatment recommendations.
Innovation Solution
A diabetes analysis system and method that performs automated analysis of glucose data from interstitial fluid, identifies hypoglycemic events, classifies their types, and provides treatment recommendations by applying pattern recognition and machine learning algorithms, including hypoglycemia and recoil classification, cluster analysis, and integration with HbA1c data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) are used to monitor glucose levels, then the ability to detect hypoglycemic events is improved, but the complexity of handling and analyzing the large amount of generated data increases
Solution Approach 1:
The patent segments the complex data analysis task into distinct functional modules: data reception module, data preparation module, hypoglycemia identification module, and analysis module. Each module handles a specific aspect of the data processing pipeline, making the overall system more manageable and less complex while maintaining high reliability in hypoglycemic event detection.
2Productivity
If automated analysis and interpretation of glucose data is implemented, then the efficiency of identifying hypoglycemic events is improved, but the device complexity increases
Solution Approach 1:
The system implements automated self-service through the hypoglycemia identification module that automatically searches received glucose-related data to identify hypoglycemic events without requiring manual analysis. The system performs computer-implemented automatic search and pattern recognition, enabling high efficiency in event identification while the modular architecture keeps complexity manageable.
3Device complexity
If manual analysis of glucose data is performed, then the device complexity is reduced, but the loss of information and underutilization of data increases
Solution Approach 1:
The patent replaces manual mechanical analysis with automated computer-implemented analysis systems. The hypoglycemia identification module uses automated search algorithms and pattern recognition to analyze glucose data, ensuring complete utilization of available data without information loss that would occur with manual sampling or selective analysis.
4Measurement precision
If comprehensive automated analysis with pattern recognition and machine learning is implemented, then the accuracy of treatment recommendations is improved, but the device complexity and computational requirements increase
Solution Approach 1:
The system performs preliminary data preparation and processing before the main analysis phase. The data preparation module pre-processes received glucose data by filtering, validating, and organizing it in advance, which reduces the computational burden during the actual hypoglycemia identification and analysis phases, thereby improving accuracy while managing complexity.
Data Source
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AI summary
Diabetes analysis system for analysis and interpretation of data related to glucose level (GL) in blood, the system is to be applied to determine a treatment recommendation to a patient. The analysis system comprises an input module (2) configured to receive GL related data (4) from measurements of interstitial fluid in subcutaneous tissue. The system further comprises a hypoglycemia identification module (6) configured to identify hypoglycemic events by performing a computer-implemented automatic search of said received GL related data, wherein all uninterrupted glucose levels less than a predetermined level, e.g. glucose levels < 3.5 mmol/L, in a same time series will be considered as one hypoglycemic event, and a hypoglycemia classification module (8) configured to analyze, for each identified hypoglycemic event, the glucose data during a predetermined first time period, e.g. three hours, preceding the hypoglycemic event, to determine the glucose level during the first time period, wherein the hypoglycemia classification module is configured to determine the type (10) of hypoglycemia event, based upon the glucose level during the first time period, by applying a computer-implemented pattern search procedure on a predetermined hypoglycemic classification scheme including different types of hypoglycemia, in order to identify the underlying cause of hypoglycemia.