NDLI: Evidence-based medicine, the essential role of systematic reviews, and the need for automated text mining tools

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AI, biomedicine and the NIH

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Evidence-based medicine, the essential role of systematic reviews, and the need for automated text mining tools

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Evidence-based medicine, the essential role of systematic reviews, and the need for automated text mining tools

Content Provider	ACM Digital Library
Author	Yu, Philip S. Davis, John M. Cohen, Aaron M. Adams, Clive E. Duggan, Lorna Smalheiser, Neil R. Meng, Weiyi McDonagh, Marian Yu, Clement
Abstract	High quality, cost-effective medical care requires consideration of the best available, most appropriate evidence in the care of each patient, a practice known as Evidence-based Medicine (EBM). EBM is dependent upon the wide availability and coverage of accurate, objective syntheses called evidence reports (also called systematic reviews). These are compiled by a time and resource-intensive process that is largely manual, and that has not taken advantage of many of the advances in information processing technologies that have assisted other textual domains. We propose a specific text-mining based pipeline to support the creation and updating of evidence reports that provides support for the literature collection, collation, and triage steps of the systematic review process. The pipeline includes a metasearch engine that covers both bibliographic databases and selected "grey" literature; a module that classifies articles according to study type; a module for grouping studies that are closely related (e.g. that derive from the same underlying clinical trial or same study cohort); and an automated system that ranks publications according to the likelihood that they will meet inclusion criteria for the report. The proposed pipeline will also enable groups performing systematic review to reuse tools and models created by other groups, and will provide a test-bed for further informatics research to develop improved tools in the future. Ultimately, this should increase the rate that high-quality systematic reviews and meta-analyses can be generated, accessed and utilized by clinicians, patients, care-givers, and policymakers, resulting in better and more cost-effective care.
Starting Page	376
Ending Page	380
Page Count	5
File Format	PDF
ISBN	9781450300308
DOI	10.1145/1882992.1883046
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-11-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Information storage and retrieval Text-mining Evidence-based medicine
Content Type	Text
Resource Type	Article

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