Health Condition Prediction via Data View Segmentation
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Solution Overview
Problem
There is a growing need for effective methods to predict and diagnose health conditions such as cardiovascular diseases, high blood pressure, and diabetes in young people, as these conditions have increased in recent years, and existing technologies lack efficient systems for early risk assessment and classification.
Innovation Solution
A system and method utilizing micro-processors to categorize human subjects into health condition categories by transforming data views into a second data structure and training a classifier based on this structure, which classifies subjects and sends the classification to computing devices for display, incorporating techniques like Collective Matrix Factorization and Bayesian learning for data transformation and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a mathematical model is developed to predict health conditions, then the capability to predict risk of diseases is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the health data into multiple categories (demographic data, lifestyle data, clinical data, laboratory data, imaging data) and processes them through separate data views. This segmentation allows the complex prediction model to handle diverse data types systematically, improving prediction capability while managing system complexity through modular data organization.
Solution Approach 2:
The system creates a universal data processing framework that can handle multiple types of health data through a common architecture. The data views and transformation mechanisms serve multiple functions: data organization, analysis, and prediction across different health conditions, thereby improving predictive capability without proportionally increasing system complexity.
2Measurement precision
If multiple data types are integrated for comprehensive health assessment, then the accuracy of health condition classification is improved, but the data processing complexity increases
Solution Approach 1:
The patent divides heterogeneous health data into distinct data views (demographic, lifestyle, clinical, laboratory, imaging) and processes them separately before integration. This segmentation enables accurate classification by maintaining the structural integrity of different data types while reducing the complexity of processing them collectively.
Solution Approach 2:
The system introduces data views as intermediary structures between raw data and the classification model. These data views serve as mediators that organize and transform multiple data types into a unified representation, improving classification accuracy while simplifying the data processing architecture through standardized transformation mechanisms.
3Productivity
If a classifier is trained using transformed data structures, then the efficiency of health risk prediction is improved, but the computational requirements increase
Solution Approach 1:
The patent performs data transformation and organization into data views as a preliminary action before training the classifier. This preliminary processing efficiency-izes the data structure, allowing the classifier to operate more efficiently on transformed data. The computational resources are invested upfront in data preparation, reducing the computational burden during actual prediction operations.
Data Source
AI summary
Disclosed are methods and systems for classifying one or more human subjects in one or more categories indicative of a health condition of the one or more human subjects. The method includes categorizing one or more parameters of each of the one or more human subjects in one or more data views based on a data type of each of the one or more parameters. A data view corresponds to a first data structure storing a set of parameters categorized in the data view, associated with each of the one or more human subjects. The one or more data views are transformed to a second data structure representative of the set of parameters across the one or more data views. Thereafter, a classifier is trained based on the second data structure, wherein the classifier classifies the one or more human subjects in the one or more categories.