AI Disease Prediction Model Segmentation for Accuracy
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Solution Overview
Problem
Current technologies lack an efficient method for monitoring and managing disease progression using user-generated health data, particularly in providing accurate disease prediction and monitoring through AI-based models.
Innovation Solution
A predictive analysis method and apparatus that utilize AI-based disease prediction and monitoring models to process user data, generating disease prediction and monitoring data, and allowing for model replacement based on performance comparison to achieve accurate predictions and monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If AI-based disease prediction and monitoring models are used to process user data, then disease prediction accuracy and monitoring effectiveness are improved, but device complexity and computational requirements increase
Solution Approach 1:
The system divides the AI model processing into separate components: a disease prediction AI model for predicting disease occurrence and a disease monitoring AI model for tracking disease progression. This segmentation allows each model to be optimized for its specific function while reducing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces a disease management device as an intermediary that receives user data, processes it through the AI models, and generates disease management data. This intermediary layer simplifies the interaction between users and complex AI systems by providing a user-friendly interface while handling the computational complexity in the background.
2Reliability
If multiple AI models are trained and performance compared to achieve most accurate predictions, then disease monitoring effectiveness is improved, but loss of time and computational resources increase
Solution Approach 1:
The system implements feedback mechanisms where disease management data generated by the AI models is used to refine and retrain the models. This continuous feedback loop improves monitoring effectiveness over time while optimizing the training process to reduce redundant computational efforts.
Solution Approach 2:
The patent employs parameter changes in the AI models, allowing dynamic adjustment of model parameters based on performance metrics. This enables the system to adapt to changing disease patterns and user data characteristics, improving reliability while optimizing training efficiency through parameter optimization rather than complete model retraining.
Data Source
AI summary
A predictive analysis method for disease progression monitoring and management and an apparatus for performing the method can include receiving, by a disease management device, user data and generating, by the disease management device, user disease management data based on the user data. The disease management data can include disease prediction data for a user or disease monitoring data for the user.