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Extracting epidemiologic exposure and outcome terms from literature using machine learning approaches
Yanxin Lu1, Hua Xu, Neeraja B Peterson
1Department of Human Anatomy, Histology and Embryology, Fudan University, 138 Yi Xue Yuan Road, Shanghai 200032, China. yanxin.m.lu@gmail.com
International Journal of Data Mining and Bioinformatics
|November 20, 2012
Summary
This study introduces a system using natural language processing and machine learning to extract epidemiologic exposures and outcomes from scientific literature, improving data accessibility for knowledge bases.
Area of Science:
- Epidemiology
- Bioinformatics
- Natural Language Processing
Background:
- Epidemiologic information is often trapped in unstructured text, limiting its use in computable knowledge bases.
- Developing systems to automatically extract key concepts from literature is crucial for advancing research.
Purpose of the Study:
- To develop and evaluate a system for extracting epidemiologic exposures and outcomes from text.
- To enhance the creation of computable knowledge bases from scientific literature.
Main Methods:
- A system combining a natural language processing (NLP) engine and a machine learning (ML) classifier was developed.
- Four ML algorithms were compared using a manually annotated dataset for performance evaluation.
Main Results:
- The system achieved an 82.0% F-measure for extracting exposure terms.
- The system achieved a 70% F-measure for extracting outcome terms.
Conclusions:
- Automated extraction of epidemiologic terms is feasible and effective.
- The developed system can significantly contribute to building structured knowledge bases from published research.
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