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.