Bayesian Network Construction for U-Health Disease Deduction
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Solution Overview
Problem
Existing methods for constructing Bayesian networks for disease diagnosis are labor-intensive and result in overly complex networks due to the inclusion of unnecessary ontology classes, leading to inefficient data analysis and prolonged processing times.
Innovation Solution
A method is developed to automatically construct a Bayesian network by analyzing U-Health information, setting meta-models for cause-and-effect relationships, and selecting specific ontologies to generate a network suitable for disease deduction, using a system with ontology management, meta-model management, abstraction level setting, and Bayesian network node generation units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all ontology classes are converted to Bayesian network nodes, then the Bayesian network can represent comprehensive information, but the network becomes huge in scale and overly complicated
Solution Approach 1:
The patent extracts only the necessary ontology classes related to the specific disease from the comprehensive ontology set. This is achieved by identifying and selecting relevant classes that have semantic relationships with the target disease, thereby constructing a simplified Bayesian network that maintains diagnostic reliability while reducing network scale and complexity.
Solution Approach 2:
The patent segments the comprehensive ontology into disease-specific subsets by establishing semantic relationships between ontology classes and the target disease. This segmentation allows the construction of a focused Bayesian network that contains only the relevant nodes and edges necessary for specific disease diagnosis, separating essential information from unnecessary complexity.
2Reliability
If comprehensive ontologies are used for disease deduction, then diagnostic coverage is improved, but data analysis time increases
Solution Approach 1:
The patent extracts only the ontology classes that are semantically related to the specific disease from the comprehensive ontology. By removing irrelevant classes and their associated nodes from the Bayesian network, the data analysis process is significantly accelerated while maintaining comprehensive diagnostic coverage for the target disease.
Solution Approach 2:
The patent implements a dynamic ontology selection mechanism that adapts the Bayesian network structure based on the specific disease being diagnosed. This dynamic approach allows the system to efficiently configure the network for each diagnostic task, optimizing the balance between diagnostic coverage and processing speed.
3Measurement precision
If manual construction of Bayesian network nodes is performed by specialists, then diagnostic accuracy is improved, but labor requirements increase
Solution Approach 1:
The patent implements an automated system that performs the functions previously requiring specialist intervention. The system automatically identifies relevant ontology classes, establishes semantic relationships, and constructs the Bayesian network structure without manual input, thereby maintaining diagnostic accuracy while eliminating labor-intensive manual construction processes.
Solution Approach 2:
The patent replaces the manual mechanical process of specialist-driven node construction with an automated computational system. This substitution uses algorithms to analyze semantic relationships, select relevant ontology classes, and automatically generate the Bayesian network structure, achieving the same diagnostic accuracy without human labor.
Data Source
AI summary
A method for constructing a database to deduce a disease and constructing a Bayesian network for U-Health application, the method including the steps of analyzing U-Health information, which includes a disease, a symptom, and a treatment, and constructing a plurality of U-Health ontologies required for service provision, setting a meta-model defining cause-and-effect relationships between the constructed U-Health ontologies and selecting at least two specific U-Health ontologies from among the plurality of U-Health ontologies, setting the selected U-Health ontologies as nodes, and generating a Bayesian network by applying the meta-model to the set nodes.