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Head and Tail Entity Fusion Model in Medical Knowledge Graph Construction: Case Study for Pituitary Adenoma
JMIR Medical Informatics
|May 31, 2021
Summary
A novel data fusion method creates a high-quality knowledge graph for pituitary adenoma (KGPA). This KGPA aids physicians by integrating diverse medical data for improved clinical practice and knowledge discovery.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Data Science
Background:
- Pituitary adenoma is a common yet challenging central nervous system tumor.
- Diagnosis and treatment difficulties, including misdiagnosis and recurrence, are prevalent.
- A shortage of experienced neurosurgeons necessitates advanced support tools.
Purpose of the Study:
- To develop a data fusion method for integrating heterogeneous medical data.
- To construct a comprehensive knowledge graph for pituitary adenoma (KGPA).
- To enable knowledge discovery and support clinical decision-making.
Main Methods:
- Developed a framework for medical knowledge graph construction.
- Manually designed the KGPA schema.
- Extracted information from Chinese electronic medical records (CEMRs) and health portals using NLP and web scraping.
- Implemented an entity fusion method for heterogeneous data integration.
Main Results:
- Extracted data from 300 CEMRs and 4 health portals.
- Achieved high F1 scores for head (97.32%) and tail (98.57%) entity fusions.
- Evaluated KGPA triples, demonstrating 95.4% accuracy.
Conclusions:
- Introduced an effective approach for fusing triples from diverse data sources to build knowledge graphs.
- The constructed KGPA contains high-quality data.
- The KGPA can significantly assist physicians in clinical practice.
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