Automating the process of critical appraisal and assessing the strength of evidence with information extraction technology.
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 ar...
| Publicado en: | Journal of Evaluation in Clinical Practice Vol. 17; no. 4; pp. 832 - 839 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Aug2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104666228&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104666228 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13561294 EV1 jtl: Journal of Evaluation in Clinical Practice issn: 13561294 maglogo: Y pubinfo: dt: Aug2011 vid: 17 iid: 4 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104666228 63476151 10.1111/j.1365-2753.2011.01712.x NLM21707873 104666228 ppf: 832 ppct: 7 formats: tig: atl: Automating the process of critical appraisal and assessing the strength of evidence with information extraction technology. aug: au: Lin, Jou-Wei Chang, Chia-Hsuin Lin, Ming-Wei Ebell, Mark H. Chiang, Jung-Hsien affil: Attending Cardiologist, Cardiovascular Center, National Taiwan University Hospital Yun-Lin Branch, Dou-Liou City, Taiwan and Associate Professor, Department of Medicine, National Taiwan University College of Medicine and Hospital, Taipei, Taiwan sug: subj: Abstracting and Indexing Methods Medical Practice, Evidence-Based Information Retrieval Data Analysis, Computer Assisted Human Decision Trees Utilization Neural Networks (Computer) Utilization Data Mining Study Design Cardiovascular Diseases Confidence Intervals Relative Risk Odds Ratio Funding Source ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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