Personalized Neural Network Models for Consistent Bio-Information Prediction
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Solution Overview
Problem
Deep learning models used in the medical field often produce inconsistent results for individual users due to variations in bio-information acquisition methods and user-specific biological characteristics, necessitating a personalized approach.
Innovation Solution
A method for acquiring a user-customized neural network model that updates itself based on user-specific bio-information, including first bio-information acquired non-invasively and second bio-information acquired invasively or by medical professionals, through continuous learning processes to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a pre-trained deep learning model is used for medical diagnosis, then the model can provide real-time health analysis without specialized medical personnel, but the model produces inconsistent results for individual users due to biological variations
Solution Approach 1:
The system performs preliminary personalization by updating the neural network model with user-specific bio-information before making diagnostic predictions. This preliminary adaptation to individual user characteristics ensures that the model accounts for biological variations before providing real-time analysis, thereby maintaining both productivity and reliability
Solution Approach 2:
The neural network model is designed to be dynamic and adaptable, continuously updating its parameters based on newly acquired user-specific bio-information. This dynamic personalization allows the model to maintain consistent and reliable diagnostic results for each individual user while preserving real-time analysis capabilities
2Ease of operation
If a pre-trained deep learning model is provided to individual users, then the model can be deployed widely without specialized personnel, but the model may produce different results for the same type of bio-information across different users
Solution Approach 1:
The neural network model performs self-personalization by automatically updating its parameters using user-specific bio-information that is easily accessible through mobile devices. This self-service mechanism allows the model to maintain high prediction accuracy for each user without requiring specialized medical personnel for calibration, thus preserving ease of operation while improving measurement precision
3Reliability
If the neural network model is updated continuously with new user data, then the model becomes personalized and accurate for individual users, but the computational resources and time required for updating increase
Solution Approach 1:
The system implements periodic updating of the neural network model rather than continuous updating. The model is updated at scheduled intervals or when sufficient new user-specific bio-information is accumulated, balancing the need for personalization accuracy with the constraint of computational time and resources
Data Source
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AI summary
According to one embodiment of the present disclosure, there is disclosed a method of acquiring a user-customized neural network model that predicts bio-information of a user, the method being performed by a computing device including at least one processor, the method including: acquiring a neural network model trained to predict second bio-information of a different type from that of first bio-information based on the first bio-information; and, when new data for the user is acquired, updating the neural network model based on the acquired new data so that the neural network model is personalized for the user; wherein the new data includes at least one of first bio-information for the user and second bio-information for the user.