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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/528

Title: Finding the best evidence in biomedical literature for evidence-based medicine
Authors: Chen, Yunan
Keywords: Information retrieval;Evidence based medicine;Medical informatics
Issue Date: 6-Sep-2005
Series/Report no.: IST Research Day 2005 posters;no. 287C
Abstract: Background: Evidence-based Medicine (EBM) is characterized by integrating individual clinical expertise with the best available external clinical evidence from systematic research. But the large amount of available biomedical literature gives healthcare practitioners much difficulty in locating the best evidence regarding each clinical question. Methods: In this study, we combined information visualization techniques with bibliographic tools to automatically extract the best external evidence out of the vast body of medical literature. We visualized the evolution of Nonsteroidal anti-inflammatory drugs (NSAID) research. Co-citation and co-keyword patterns were visualized in cluster views and time zone views of the NSAID research over 15 years (1990-2005). Our dataset combined Medline and Web of Science together. It included the MeSH terms and Publication Types (PT) such as important EBM types of meta-analysis and randomized controlled trails form Medline and cited references information from Web of Science. Results: The visualization shows 5 clusters of NASID research: 1) Alzheimer Disease, 2) Cyclooxygenase, 3) Colonic Neoplasms, 4) Adverse Effects of Selective Inhibitors, and 5) Adverse Effects of Traditional, Non-selective Inhibitors. These clusters perfectly match the previous review articles and expert opinion. The time zone graph reveals more valuable information. The keyword of “pump inhibitors” peaked twice in 1999 and again in 2004, corresponding to the emerging trend in COX-2 inhibitors research in 1999 and the withdrawal of Vioxx - one of COX-2 inhibitors by Merck & Co in 2004. The success of this case study will lead to the future design of visualization-based evidence searching tools for EBM.
URI: http://hdl.handle.net/1860/528
Appears in Collections:Research Day Posters (IST)

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