Physiological Data Pattern Recognition with Contextualization
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Solution Overview
Problem
Existing methods for continuously monitoring blood glucose levels in diabetes patients are limited by the inability to accurately detect patterns and contextualize data, leading to incomplete or incorrect analysis, which can result in missed events and reduced reliability in predicting future trends.
Innovation Solution
A method and device that analyze continuously monitored physiological measurement values by identifying common patterns between present and historical data, requesting user contextualization, and storing contextualized data to enhance pattern recognition and forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous glucose monitoring is performed without pattern recognition and contextualization, then measurement coverage is comprehensive, but analysis reliability and event detection accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and storing contextual information (meals, exercise, medication, stress) in advance before pattern analysis is needed. This preparatory data collection enables more reliable pattern recognition and event detection when analyzing glucose measurements, without requiring complex real-time processing during critical monitoring moments.
Solution Approach 2:
The patent introduces contextualization data as an intermediary element that mediates between raw glucose measurements and pattern analysis. This additional layer of information (user-provided context about meals, exercise, etc.) enhances the reliability of event detection by providing background information that helps distinguish between normal fluctuations and significant events.
2Measurement precision
If pattern recognition is performed without user contextualization, then analysis speed is maintained, but pattern recognition accuracy and event detection completeness deteriorate
Solution Approach 1:
The system implements self-service by automatically prompting users to provide contextualization information only when patterns are detected that require additional context for accurate interpretation. This on-demand approach minimizes the time users need to spend providing information while still achieving high pattern recognition accuracy when contextualization is available.
Solution Approach 2:
The patent applies partial action by selectively requesting contextualization only for specific patterns that benefit from additional context, rather than requiring users to provide contextual information for all measurements. This reduces the time burden on users while maintaining high accuracy for critical event detections.
3Loss of information
If contextualization is requested for all measurements, then data completeness is improved, but user burden and system complexity increase
Solution Approach 1:
The system performs preliminary analysis of glucose patterns to identify which measurements would benefit from contextualization before requesting user input. This selective approach ensures data completeness for critical events while minimizing user burden by only requesting contextualization when it adds value to the analysis.
Solution Approach 2:
The system serves itself by automatically determining which data points require contextualization based on pattern recognition algorithms. This self-service mechanism reduces user burden by eliminating the need for users to manually contextualize every measurement, while still achieving comprehensive data collection for relevant events.
4Measurement precision
If historical data is stored and compared for pattern recognition, then forecasting accuracy is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The system extracts only the essential features and patterns from historical data that are relevant for forecasting, rather than processing complete raw datasets. This extraction approach maintains high forecasting accuracy by focusing on critical patterns while reducing the computational energy required for data processing and comparison.
Data Source
AI summary
The application relates to a method for analyzing continuously monitored physiological measurement values of a user, the method being performed in a data processing system and comprising: providing, by a data interface, a set of present physiological measurement values, determining whether a common pattern of values is contained in both the set of present physiological measurement values and a set of historical physiological measurement values, if the common pattern of values is found, requesting the user to provide contextualization for at least the present physiological measurement values of the common pattern of values, receiving contextualization and storing contextualized data. Furthermore, the application relates to a device for analyzing continuously monitored physiological measurement values of a user.