Discriminating between empirical studies and nonempirical works using automated text classification

TitreDiscriminating between empirical studies and nonempirical works using automated text classification
Type de publicationJournal Article
Année de publication2018
AuteursLanglois, A., J-Y. Nie, J. Thomas, Q. Nha Hong, and P. Pluye
JournalResearch Synthesis Methods
Mots-clésautomated text classification, decision tree, health care, research method, support vector machine, systematic review
RésuméObjective: Identify the most performant automated text classification method (eg, algorithm) for differentiating empirical studies from nonempirical works in order to facilitate systematic mixed studies reviews. Methods: The algorithms were trained and validated with 8050 database records, which had previously been manually categorized as empirical or nonempirical. A Boolean mixed filter developed for filtering MEDLINE records (title, abstract, keywords, and full texts) was used as a baseline. The set of features (eg, characteristics from the data) included observable terms and concepts extracted from a metathesaurus. The efficiency of the approaches was measured using sensitivity, precision, specificity, and accuracy. Results: The decision trees algorithm demonstrated the highest performance, surpassing the accuracy of the Boolean mixed filter by 30%. The use of full texts did not result in significant gains compared with title, abstract, keywords, and records. Results also showed that mixing concepts with observable terms can improve the classification. Significance: Screening of records, identified in bibliographic databases, for relevant studies to include in systematic reviews can be accelerated with automated text classification.