ZHEJIANG LAB (20240212862). GENERAL MULTI-DISEASE PREDICTION SYSTEM BASED ON CAUSAL CHECK DATA GENERATION simplified abstract
Contents
GENERAL MULTI-DISEASE PREDICTION SYSTEM BASED ON CAUSAL CHECK DATA GENERATION
Organization Name
Inventor(s)
Shengqiang Chi of Hangzhou (CN)
GENERAL MULTI-DISEASE PREDICTION SYSTEM BASED ON CAUSAL CHECK DATA GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240212862 titled 'GENERAL MULTI-DISEASE PREDICTION SYSTEM BASED ON CAUSAL CHECK DATA GENERATION
Simplified Explanation: The patent application describes a multi-disease prediction system that generates causal check data to improve prediction accuracy based on causal relationships.
Key Features and Innovation:
- Tendency score calculation method based on a general tendency score network from a causality perspective.
- Generative adversarial network based on causal check for better data generation.
- General multi-disease prediction model using causal graph convolutional neural network.
- Integration of causal effect value to enhance prediction performance.
- Addresses poor model performance and low robustness due to limited training samples.
Potential Applications: The technology can be applied in healthcare for predicting multiple diseases, personalized medicine, and improving patient outcomes.
Problems Solved: The system addresses the issues of poor model performance and low robustness caused by limited training samples in predicting multiple diseases.
Benefits:
- Improved prediction accuracy.
- Enhanced model performance.
- Better understanding of causal relationships in disease prediction.
Commercial Applications: The technology can be utilized in healthcare institutions, research facilities, and pharmaceutical companies for disease prediction, treatment planning, and drug development.
Prior Art: Readers can explore prior research on causal inference, multi-disease prediction models, and generative adversarial networks in healthcare.
Frequently Updated Research: Stay updated on advancements in causal inference methods, multi-disease prediction models, and generative adversarial networks in healthcare.
Questions about Multi-Disease Prediction System: 1. How does the integration of causal effect value improve prediction performance? 2. What are the potential real-world applications of this multi-disease prediction system?
Original Abstract Submitted
disclosed is a general multi-disease prediction system based on causal check data generation. for a general scenario, the present invention provides a tendency score calculation method based on a general tendency score network from the perspective of causality; compared with the problem of poor interpretability of traditional generative adversarial networks, the present invention provides a generative adversarial network based on causal check, so that generated data better conforms to real causal logic; in view of the problem that existing graph convolutional neural networks are modeled only from the perspective of correlation, the present invention provides a general multi-disease prediction model based on a general causal graph convolutional neural network, and a causal effect value is integrated to improve the prediction performance of the general multi-disease prediction system on diseases, thereby solving the problems of poor model performance and low robustness caused by few training samples in a general scenario.
