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Novel information retrieval methods to identify citations relevant to aclinical topic can overcome the knowledge gap existing between the primaryliterature (MEDLINE) and online clinical knowledge resources such as UpToDate.Searching the MEDLINE database directly or with query expansion methods returnsa large number of citations that are not relevant to the query. The currentstudy presents a citation retrieval system that retrieves citations forevidence-based clinical knowledge summarization. This approach combines queryexpansion, concept-based screening algorithm, and concept-based vectorsimilarity. We also propose an information extraction framework for automatedconcept (Population, Intervention, Comparison, and Disease) extraction. Weevaluated our proposed system on all topics (as queries) available fromUpToDate for two diseases, heart failure (HF) and atrial fibrillation (AFib).The system achieved an overall F-score of 41.2% on HF topics and 42.4% on AFibtopics on a gold standard of citations available in UpToDate. This issignificantly high when compared to a query-expansion based baseline (F-scoreof 1.3% on HF and 2.2% on AFib) and a system that uses query expansion withdisease hyponyms and journal names, concept-based screening, and term-basedvector similarity system (F-score of 37.5% on HF and 39.5% on AFib). Evaluatingthe system with top K relevant citations, where K is the number of citations inthe gold standard achieved a much higher overall F-score of 69.9% on HF topicsand 75.1% on AFib topics. In addition, the system retrieved up to 18 newrelevant citations per topic when tested on ten HF and six AFib clinicaltopics.

A Hybrid Citation Retrieval Algorithm for Evidence-based Clinical  Knowledge Summarization: Combining Concept Extraction, Vector Similarity and  Query Expansion for High Precision