Health Prediction via Knowledge Correlation Model
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Solution Overview
Problem
Current systems lack an effective method to predict human health outcomes based on knowledge levels without requiring medical information or biological samples, and existing health questionnaires are static and not adaptable to individual knowledge profiles.
Innovation Solution
A system and method that determine a user's health outcome by correlating their knowledge of health-related assertions with health parameters, using a dynamic knowledge profile analysis and predictive modeling to provide personalized health services and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If health outcomes are predicted using traditional methods requiring medical information and biological samples, then prediction accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent extracts the essential predictive information from complex medical data by identifying and utilizing only health-related knowledge assertions. Instead of requiring full medical records and biological samples, the system extracts prediction capability from simplified knowledge-based responses, achieving accurate health outcome prediction without the complexity of traditional medical assessment systems
Solution Approach 2:
The patent replaces the mechanical/physical system of collecting biological samples and medical information with an information-based system using knowledge assertions. The health prediction function is achieved through substituting physical medical testing with cognitive knowledge assessment, eliminating the need for complex medical infrastructure while maintaining prediction capability
2Ease of operation
If static health questionnaires are used, then ease of operation is improved, but adaptability to individual knowledge profiles deteriorates
Solution Approach 1:
The patent transforms the static questionnaire into a dynamic system that adapts to individual users. The health assertion library and prediction model adjust based on each user's knowledge profile, allowing the same questionnaire structure to serve different individuals with varying knowledge levels. The system dynamically weights and interprets responses according to the user's demonstrated health knowledge
Solution Approach 2:
The patent changes the parameters of the assessment system by introducing knowledge-based weighting and individualized interpretation thresholds. Instead of fixed pass/fail criteria, the system adjusts evaluation parameters based on the user's knowledge profile, enabling the same questionnaire to adaptively assess different individuals with appropriate rigor and relevance
3Measurement precision
If comprehensive medical information is collected, then health prediction accuracy is improved, but loss of time and cost increase
Solution Approach 1:
The patent extracts only the essential predictive elements from comprehensive medical assessment by focusing on health-related knowledge assertions. Instead of collecting extensive medical history and biological data, the system extracts prediction capability from targeted knowledge questions, achieving accurate health outcome prediction in a fraction of the time required by traditional methods
Solution Approach 2:
The patent performs preliminary action by pre-identifying and storing health-related knowledge assertions in a structured library before the assessment occurs. This pre-prepared framework allows rapid evaluation of user responses against known health correlations, eliminating the need for time-consuming analysis of comprehensive medical records during the actual assessment
Data Source
AI summary
A computing system can execute a correlation model to determine correlation values for health assertions, where the correlation value for each health assertion indicates a level of correlation between the health assertion and one or more actual health outcomes. Actual health outcomes are provided by control group users who have also provided responses, either correct or incorrect, to each of the health assertions. The computing system may then provide users with a health trivia session comprised of these health assertions, and based at least in part on the performance of the user during the trivia session, generate a health profile for the user.