Personalized Healthcare Advice System Using Activity Correlation
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Solution Overview
Problem
Existing healthcare systems struggle to provide personalized health-related advice that accurately identifies the cause of symptoms by considering user activities and medical records, often leading to incorrect assumptions and potential panic situations.
Innovation Solution
A method and electronic device that receive user input on symptoms, retrieve medical records and user activities, correlate this information to identify lifestyle-related activities associated with the symptoms, and provide personalized advice on the cause of the symptoms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If healthcare systems provide personalized health advice based on user activities and medical records, then the accuracy of symptom cause identification is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex healthcare analysis task into distinct modules: one module retrieves user activities from sensors, another retrieves medical records from databases, a third correlates these data sources, and a final module generates personalized advice. This segmentation allows each module to handle specific data processing responsibilities, improving accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system introduces an intermediary correlation module that acts as a mediator between raw data sources (user activities and medical records) and the final advice generation. This intermediary processes and integrates multiple data types, transforming them into meaningful correlations that identify symptom causes accurately without requiring the entire system to handle all data processing simultaneously.
2Measurement precision
If the system retrieves and correlates multiple data sources including user activities and medical records, then the personalization and accuracy of health advice is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-retrieving and storing user activities from sensors and medical records from databases before symptom analysis is needed. This allows the correlation module to work with pre-processed data when a symptom is reported, reducing the actual analysis time while maintaining comprehensive data coverage for accurate personalized advice.
3Reliability
If the system provides comprehensive health analysis considering multiple factors, then the quality of healthcare advice is improved, but the ease of operation and user interface simplicity deteriorates
Solution Approach 1:
The system extracts the complexity from the user interaction layer and places it in the backend processing modules. Users simply input their symptom through a simple interface, while the system automatically retrieves and correlates user activities, medical records, and other data sources in the background. This extraction allows comprehensive analysis to occur without burdening the user interface with complexity.
Data Source
Figure 1~2A
Figure 2B~2C
Figure 2D
AI summary
A method and electronic device for providing personalized health-related advice are provided. The method includes receiving, at the electronic device, a user input indicating a symptom related to the user, retrieving, from the electronic device, at least one medical information of the user, detecting a user activity prior to the symptom arising, correlating the user input indicating the symptom, the at least one medical information of the user and the user activity prior to the symptom arising, and displaying advice about the symptom based on the correlation.