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A hybrid knowledge-based and data-driven approach to identifying semantically similar concepts
Rimma Pivovarov1, Noémie Elhadad
1Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, VC-5, New York, NY 10032, USA.
Journal of Biomedical Informatics
|February 1, 2012
Summary
Determining concept similarity is crucial for data mining. Our new method combines data and ontology knowledge to accurately quantify semantic similarity, achieving 92% AUC.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Knowledge Representation
Background:
- Leveraging ontological knowledge in research presents challenges in concept aggregation versus separation.
- Context-dependent concept similarity impacts dataset quality and data mining efficacy.
- Existing methods struggle to quantify nuanced semantic similarity.
Purpose of the Study:
- To develop a comprehensive method for computing semantic similarity scores between concept pairs.
- To differentiate between semantically similar concepts and merely related concepts.
- To improve dataset creation for data mining by accurately assessing concept relatedness.
Main Methods:
- Proposed a novel method combining data-driven (usage patterns in clinical notes) and ontology-driven knowledge (SNOMED-CT structure).
- Calculated a similarity score for concept pairs.
- Evaluated the method on clinical notes from chronic kidney disease patients and SNOMED-CT concepts.
Main Results:
- The proposed method effectively combines information from clinical notes and ontological structure.
- Successfully distinguished between related and semantically similar concepts, avoiding signal dilution.
- Achieved a high performance with an Area Under the Curve (AUC) of 92% when evaluated against annotated concept pairs.
Conclusions:
- The developed method offers a robust approach to quantifying context-dependent concept similarity.
- This technique enhances the creation of high-quality datasets for medical data mining.
- Accurate concept similarity assessment is vital for advancing biomedical research and clinical informatics.
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