Genotype-Based Model for Predictive Health Profiling
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Solution Overview
Problem
Conventional genetic testing techniques rely on statistical relationships between genes and traits, failing to account for complex gene interactions, thereby limiting their predictive and interventional value in understanding a person's genome and providing effective medical or wellness interventions.
Innovation Solution
A genotype-based model is employed to assign classifications to individuals based on their genetic information, using a processor to receive and analyze genetic data, assign values to genetic indicators, and predict health states or recommend interventions by categorizing individuals into specific traits or risk factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional genetic testing techniques are used that rely on statistical relationships between genes and traits, then the testing process is simple and based on established statistical methods, but the predictive value and understanding of complex gene interactions are limited
Solution Approach 1:
The patent segments the genome into multiple genetic indicators grouped into functional categories (e.g., metabolism, inflammation, hormone balance). Each indicator is assigned a value based on genetic variants, and these segmented indicators are then integrated to form a comprehensive physiological profile. This segmentation allows the system to manage complex gene interactions while maintaining analytical precision.
Solution Approach 2:
The patent transforms genetic data from simple statistical associations into functional parameter values. Each genetic indicator is converted into a quantifiable parameter that reflects its physiological impact. These parameters are then combined through mathematical models to predict health states, thereby changing the parameter representation from statistical to functional to improve predictive value.
2Loss of information
If a genotype-based model is employed to assign classifications and predict health states, then deeper understanding of causal relations between genes and traits is achieved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent introduces an intermediary layer of genetic indicators that mediate between raw genetic data and health state predictions. These indicators serve as intermediate representations that capture complex gene interactions in a structured format. The indicators are grouped into functional categories and assigned values that reflect their physiological significance, thereby reducing information loss while managing system complexity through hierarchical organization.
Solution Approach 2:
The patent adds a functional dimension to genetic analysis by organizing genetic indicators into physiological categories (metabolism, inflammation, hormone balance, etc.). This dimensional transformation allows the system to represent gene interactions in multiple functional dimensions simultaneously, improving understanding of causal relations while structuring the complexity in a manageable framework.
3Reliability
If genetic information is analyzed to provide personalized medical interventions, then the effectiveness and personalization of interventions are improved, but the time and computational resources required for analysis increase
Solution Approach 1:
The patent performs preliminary action by pre-defining genetic indicators, their functional categories, and the relationships between them before actual genetic analysis. The system establishes a framework of expected genetic variants and their physiological impacts in advance. When genetic data is received, the system quickly maps variants to pre-defined indicators and categories, significantly reducing analysis time while maintaining personalized intervention effectiveness.
Solution Approach 2:
The patent transforms genetic data into standardized parameter values that can be quickly processed and compared against established thresholds and ranges. By converting complex genetic information into simplified functional parameters, the system enables rapid assessment and intervention recommendation without sacrificing personalization or effectiveness.
Data Source
AI summary
A system for providing intervention information to a subject may be configured to perform a method including: receiving, via the processor, genetic information of the subject, the genetic information including a plurality of sets, and each set comprising one or more genes; assigning, via the processor, a value to each respective set; assigning, via the processor, a respective classification to the subject in one or more categories, wherein: each category corresponds to a respective set; and the assignment of the respective classification is based on the respective values assigned to the respective sets; obtaining, via the processor, health profile information and wellness intervention information associated with the assigned classifications of the one or more categories; and outputting, via the processor, the health profile information and the wellness intervention information to a user. The assignment of the respective classification may be predictive of the health state of the subject, and a method may include: generating a health profile of the subject based on the predicted health state from the assigned classifications for the one or more categories; and outputting the health profile of the subject.