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Retrieval and classification of dental research articles
W C Bartling1, T K Schleyer, S Visweswaran
1Center for Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA. wcb@cbmi.upmc.edu
Advances in Dental Research
|May 6, 2004
Summary
Retrieving dental and craniofacial research literature is challenging. A text classifier, Identify Patient Sets (IPS), accurately identified dental research articles using titles and abstracts, outperforming MeSH terms.
Area of Science:
- Biomedical Informatics
- Dental Research
Background:
- Literature retrieval for broad scientific topics, like dental and craniofacial research, presents significant challenges.
- Manual identification and categorization of relevant studies are time-consuming and may lack comprehensive coverage.
Purpose of the Study:
- To develop and evaluate a method for efficiently retrieving dental and craniofacial research literature.
- To assess the performance of a probabilistic text classifier in identifying relevant research articles.
Main Methods:
- Explored Medical Subject Headings (MeSH) for relevant dental and craniofacial terms.
- Searched MEDLINE and randomly sampled references.
- Dental research experts categorized articles based on title and abstract.
- Developed text classification models using Identify Patient Sets (IPS) based on word and UMLS phrase presence.
Main Results:
- The IPS text classifier, using only titles and abstracts, correctly classified 64% of dental research articles in the test set.
- When applied to the retrieved literature set, IPS achieved 71% accuracy in identifying dental research articles.
- Incorporating MeSH terms into the classification process decreased performance.
Conclusions:
- Automated text classification using tools like IPS can significantly improve the retrieval of specific research literature.
- Title and abstract text analysis is more effective for this classification task than relying solely on MeSH terms.
- Computer programs analyzing text input offer a promising approach to enhance literature corpus retrieval.
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