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.

ABSTRACT

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.