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  4. Automating the process of critical appraisal and assessing the strength of evidence with information extraction technology
 
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Automating the process of critical appraisal and assessing the strength of evidence with information extraction technology

Journal
Journal of Evaluation in Clinical Practice
Journal Volume
17
Journal Issue
4
Pages
832-838
Date Issued
2011
Author(s)
JOU-WEI LIN  
CHIA-HSUIN CHANG  
Lin M.-W.
Ebell M.H.
Chiang J.-H.
DOI
10.1111/j.1365-2753.2011.01712.x
URI
https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961023449&doi=10.1111%2fj.1365-2753.2011.01712.x&partnerID=40&md5=9b6cb6475490454951d2424830b29cf0
https://scholars.lib.ntu.edu.tw/handle/123456789/517180
Abstract
Background Critical appraisal, one of the most crucial steps in the practice of evidence-based medicine, is expertise-dependent and time-consuming. The objective of this study was to develop and evaluate an automated text-mining system that could determine the evidence level provided by a medical article. Methods A text processor was designed and built to interpret the abstracts of medical literature. The system extracted information about: (1) the impact factor of the journal; (2) study design; (3) human subject involvement; (4) number of subjects; (5) P-value; and (6) confidence intervals. We used a classification tree algorithm (C4.5) to create a decision tree using supervised classification. Each article was categorized into evidence level A, B or C, and the output was compared to that determined by domain experts (the reference standard). Results We used a corpus of 3180 cardiovascular disease original research articles, of which 1108 were previously assigned evidence level A, 1705 level B and 367 level C by domain experts. The abstracts were analysed by our automated system and an evidence level was assigned. The algorithm accurately classified 85% of the articles. The agreement between computer and domain experts was substantial (κ-value: 0.78). Cross-validation showed consistent results across repeated tests. Conclusion The automated engine accurately classified the evidence level. Misclassification might have resulted from incomplete information retrieval and inaccurate data extraction. Further efforts will focus on assessing relevance and using additional study design features to refine evidence level classification. ? 2011 Blackwell Publishing Ltd.
SDGs

[SDGs]SDG3

Other Subjects
algorithm; article; automation; cardiovascular disease; computer; decision tree; evidence based medicine; information technology; medical literature; priority journal; study design; Abstracting and Indexing as Topic; Algorithms; Automation; Cardiovascular Diseases; Data Mining; Evaluation Studies as Topic; Evidence-Based Medicine; Humans; Journal Impact Factor; Periodicals as Topic; Taiwan
Type
journal article

臺大位居世界頂尖大學之列,為永久珍藏及向國際展現本校豐碩的研究成果及學術能量,圖書館整合機構典藏(NTUR)與學術庫(AH)不同功能平台,成為臺大學術典藏NTU scholars。期能整合研究能量、促進交流合作、保存學術產出、推廣研究成果。

To permanently archive and promote researcher profiles and scholarly works, Library integrates the services of “NTU Repository” with “Academic Hub” to form NTU Scholars.

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開放取用是從使用者角度提升資訊取用性的社會運動,應用在學術研究上是透過將研究著作公開供使用者自由取閱,以促進學術傳播及因應期刊訂購費用逐年攀升。同時可加速研究發展、提升研究影響力,NTU Scholars即為本校的開放取用典藏(OA Archive)平台。(點選深入了解OA)

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