Personal Physiological Pattern Profile for Healthcare Event Prediction
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Solution Overview
Problem
Current portable devices, such as wearable and mobile devices, are unable to predict medical or healthcare events like falls, heart attacks, or strokes in real-time, limiting the timely provision of appropriate medical treatment.
Innovation Solution
A system and method for continuous monitoring of physiological indicators using sensors and machine learning algorithms to create a personal physiological pattern profile, allowing for real-time prediction of healthcare events by comparing current data to baseline patterns and issuing alerts when deviations exceed predefined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If portable devices collect and provide cumulative information about health data, then the quantity of health data is improved, but the ability to predict healthcare events deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing health indicator measurements over time before actual healthcare events occur. This creates a baseline dataset that enables future prediction and comparison, allowing the device to proactively identify deviations from normal patterns rather than merely reporting historical statistics.
Solution Approach 2:
The system implements feedback by comparing real-time health indicator measurements against stored baseline patterns and providing alerts when deviations exceed predetermined thresholds. This closed-loop feedback mechanism transforms raw data collection into actionable predictive insights, enabling the device to detect potential healthcare events by recognizing pattern deviations.
2Loss of information
If portable devices provide cumulative health information, then information completeness is improved, but real-time prediction capability deteriorates
Solution Approach 1:
The system maintains continuous monitoring and comparison operations, constantly analyzing incoming health indicator data against stored baseline patterns. This continuous analytical process ensures both complete information retention and immediate detection capability, as the system never stops comparing current measurements to establish patterns without time loss.
Solution Approach 2:
By pre-storing baseline patterns and predetermined thresholds before actual events occur, the system eliminates processing delays during critical detection moments. The preliminary establishment of reference data enables immediate real-time comparison and prediction without sacrificing information completeness.
3Measurement precision
If portable devices collect comprehensive health data, then data accuracy is improved, but the ability to detect deviations from normal behavior deteriorates
Solution Approach 1:
The system transforms raw health indicator measurements into meaningful deviations by comparing them against personalized baseline patterns. This parameter transformation approach converts accurate but meaningless absolute values into relative deviation metrics that are easily detectable and interpretable, solving the difficulty of identifying abnormal patterns in comprehensive data.
Solution Approach 2:
By implementing continuous feedback comparison between current measurements and baseline patterns, the system automatically highlights deviations without manual analysis. This feedback mechanism simplifies deviation detection by providing clear signals when measured parameters diverge from established normal ranges.
Data Source
AI summary
A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.