Disease Prediction System Integrating Biosignal Data and Medical Knowledge
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Solution Overview
Problem
Current ICT-based healthcare systems face challenges in accurately predicting diseases using only biosignal sensor data, as they lack consideration for personal information and real-time biometric data, and relying solely on medical knowledge bases results in general, non-personalized analysis.
Innovation Solution
A system and method that converges biosignal data from wearable sensors with a medical knowledge base using a pre-trained prediction model, enhancing prediction accuracy by linking biosignal data with domain-specific medical knowledge for personalized analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only biosignal sensor data is used for disease prediction, then the system complexity is low, but the prediction accuracy is insufficient
Solution Approach 1:
The patent merges data-based machine learning analysis with knowledge-based medical domain information into a unified disease prediction system. The convergence unit integrates biosignal data processing with medical knowledge base queries, combining statistical patterns from machine learning with domain-specific medical reasoning to achieve higher prediction accuracy while maintaining manageable system complexity through modular architecture
Solution Approach 2:
The system uses a composite approach by combining two distinct analytical methodologies: data-driven machine learning models that process biosignal patterns and knowledge-driven medical reasoning systems that apply domain expertise. This composite framework leverages the strengths of both approaches, where machine learning identifies patterns and the knowledge base provides medical context and validation, resulting in more accurate and interpretable predictions
2Loss of information
If data-based machine learning analysis is used, then large amounts of biosignal data can be processed, but the analysis results lack explanation and multi-angle analysis information
Solution Approach 1:
The medical knowledge base acts as an intermediary that bridges the gap between raw machine learning predictions and clinically meaningful interpretations. When the convergence unit receives prediction results, it queries the knowledge base to retrieve relevant medical domain information, disease characteristics, and diagnostic criteria, thereby explaining the predictions in medically contextually accurate terms without significantly increasing processing time
Solution Approach 2:
The system implements a feedback mechanism where initial predictions from the machine learning model trigger queries to the medical knowledge base, which then provides additional context and multi-angle analysis information. This feedback loop enriches the prediction results with explanatory details about disease characteristics, risk factors, and diagnostic considerations, enabling both efficient processing and comprehensive interpretation
3Adaptability or versatility
If only medical knowledge base is used for analysis, then domain-specific medical knowledge is applied, but the analysis is general and not personalized
Solution Approach 1:
The system applies local quality by customizing the medical knowledge base queries based on individual user characteristics extracted from biosignal data. The convergence unit adapts the general medical knowledge to specific users by incorporating their personal health data, demographic information, and real-time biosignal measurements, thereby transforming generic medical information into personalized analysis results that address each user's specific health context and risk profile
Data Source
AI summary
Provided is a system for predicting disease based on biosignal data and medical knowledge base convergence. The system includes a first system unit configured to receive, from a user terminal, biosignal data collected from at least one sensor for sensing a biosignal, and calculate a disease score from the biosignal data based on a pre-trained prediction model, a second system unit configured to provide medical knowledge data for the first system unit, analyze a query input from the user terminal to provide a corresponding response, and augment the medical knowledge data based on the query and response; and a unified distributed repository that includes a database for enqueuing the biosignal data, a manager database for storing additional information of a user, and a medical knowledge base for storing predetermined medical knowledge data.