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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
Published on: April 14, 2023
Ontology-oriented diagnostic system for traditional Chinese medicine based on relation refinement
Peiqin Gu1, Huajun Chen, Tong Yu
1College of Computer Science, Zhejiang University, Hangzhou 310027, China.
Computational and Mathematical Methods in Medicine
|March 28, 2013
Summary
This study introduces an Ontology-oriented Diagnosis System (ODS) for Traditional Chinese Medicine (TCM). The ODS models complex TCM knowledge, achieving 92% accuracy in computer-aided diagnosis for common clinical cases.
Area of Science:
- Computer Science
- Medical Informatics
- Traditional Chinese Medicine
Background:
- Traditional Chinese Medicine (TCM) diagnosis is complex and difficult to apply in computer-aided systems.
- Modeling TCM knowledge is crucial for developing effective diagnostic tools.
Purpose of the Study:
- To develop an Ontology-oriented Diagnosis System (ODS) for computer-aided TCM diagnosis.
- To model the fuzzy relationships between symptoms and syndromes in TCM.
Main Methods:
- Defined TCM diagnosis as discovering fuzzy relations between symptoms and syndromes.
- Created an ODS using a well-defined ontology of syndromes to create a machine-interpretable model.
- Collected clinical data for feature selection and trained the system for syndrome differentiation.
- Analyzed clinical cases to establish statistical mapping between syndromes and symptoms, refining the ontology.
Main Results:
- The ODS achieved 92% diagnostic accuracy, with results aligning perfectly with expert expectations.
- Demonstrated superior diagnostic accuracy compared to non-ontology methods.
- Successfully modeled implicit relationships among syndromes into a machine-interpretable format.
Conclusions:
- The Ontology-oriented Diagnosis System (ODS) effectively models complex TCM knowledge for computer-aided diagnosis.
- The ODS offers a viable approach for enhancing TCM diagnostic accuracy and accessibility.
- The system's web interface enables user self-diagnosis, improving accessibility to TCM expertise.
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