Multi-Input Health Risk Prediction for Personalized Management
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Solution Overview
Problem
There is a lack of user-friendly, personalized digital tools that effectively translate complex health data into actionable insights for proactive health management, failing to provide a comprehensive holistic picture of an individual's health status and tailored preventive interventions.
Innovation Solution
A device and method utilizing a trained model that integrates diverse user data types, including biometric and environmental data, through a multi-input machine learning model to predict personalized risk scores and provide health-related instructions, leveraging AI and virtual twins for dynamic health monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive health data collection and complex AI modeling are implemented, then prediction accuracy and personalization improve, but device complexity and implementation difficulty increase
Solution Approach 1:
The system segments health data collection into multiple specialized input channels (biometric sensors, environmental sensors, user surveys, electronic health records) and processes different data types through separate processing pipelines before integration. This modular segmentation allows complex AI modeling to be implemented as composed of manageable, independent modules that can be developed, validated, and maintained separately while achieving comprehensive health assessment accuracy.
Solution Approach 2:
The patent introduces intermediate processing layers including data normalization modules, feature extraction components, and risk score aggregation mechanisms that mediate between raw multi-source health data and final predictions. These intermediaries transform complex heterogeneous data into standardized formats suitable for AI modeling, reducing implementation complexity while preserving prediction accuracy.
2Adaptability or versatility
If multiple data sources and types are integrated, then holistic health assessment improves, but data processing complexity increases
Solution Approach 1:
The system implements a universal data processing framework that handles multiple data types (biometric, environmental, survey, EHR data) through common processing pipelines. The AI model is designed with multi-input capabilities that can accommodate various data sources simultaneously, allowing holistic health assessment across multiple dimensions without requiring separate processing systems for each data type.
Solution Approach 2:
The patent applies parameter transformation techniques to convert diverse health data into standardized risk score parameters. Different data sources are transformed into comparable parametric representations that can be integrated into unified risk predictions, simplifying the processing of multi-source data while maintaining comprehensive health assessment capabilities.
Data Source
AI summary
A device and associated computer-implemented method for obtaining a trained model for prediction of at least one risk score of a user for developing an abnormality with respect to a health status of the user, and to a device and associated method for assisting the user by providing at least one health-related instruction for improving a health status of the user, comprising using a trained model for prediction obtained by the device.