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Semantic Processing to Support Clinical Guideline Development
Clinical practice guidelines are one of the main resources for communicating evidence-based practice to health professionals. During guideline development, questions that express a knowledge gap are answered by finding relevant citations in MEDLINE and other biomedical databases. Determining citation relevance involves extensive manual review. We propose an automated method for finding relevant citations based on guideline question classification, semantic processing, and rules that match question classes with semantic predications. In this initial study, we focused on a pediatric cardiovascular risk factor guideline. The overall performance of the system was 40% recall, 88% precision (F0.5-score 0.71), and 98% specificity. We show that relevant and nonrelevant citations have clinically different semantic characteristics and suggest that this method has the potential to improve the efficiency of the literature review process in guideline development.