Application of text mining to the development and validation of a geographic search filter to facilitate evidence retrieval in Ovid MEDLINE: An example from the United States.

Background: Given the increasing volume of published research in bibliographic databases, efficient retrieval of evidence is crucial and represents an opportunity to integrate novel techniques such as text mining. Objectives: To develop and validate a geographic search filter for identifying research from the United States (US) in Ovid MEDLINE. Methods: US and non‐US citations were collected from bibliographies of evidence‐based reviews. Citations were partitioned by US/non‐US status and randomly divided to a training and testing set. Using text mining, common one‐ and two‐word terms in title/abstract fields were identified, and frequencies compared between US/non‐US citations. Results: Common US‐related terms included (as ratio of frequency in US/non‐US citations) US populations and geographic terms [e.g., 'Americans' (15.5), 'Baltimore' (20.0)]. Common non‐US terms were non‐US geographic terms [e.g., 'Japan' (0.04), 'French' (0.05)]. A search filter was developed with 98.3% sensitivity and 82.7% specificity. Discussion: This search filter will streamline the identification of evidence from the US. Periodic updates may be necessary to reflect changes in MEDLINE's controlled vocabulary. Conclusion: Text mining was instrumental to the development of this search filter. A novel technique generated a gold standard set comprising >20,000 citations. This method may be adapted to develop subsequent geographic search filters. [ABSTRACT FROM AUTHOR]