Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC).
Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents "consistent with cancer" to evaluate system performance in the domains of col...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 17; no. 4; pp. 375 - 383 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jul2010
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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=105043424&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105043424 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jul2010 vid: 17 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 105043424 105043424 NLM20595303 2010701901 10.1136/jamia.2009.001412 NLM20595303 105043424 ppf: 375 ppct: 8 formats: tig: atl: Evaluation of a generalizable approach to clinical information retrieval using the automated retrieval console (ARC). aug: au: D'Avolio LW Nguyen TM Farwell WR Chen Y Fitzmeyer F Harris OM Fiore LD D'Avolio, Leonard W Nguyen, Thien M Farwell, Wildon R Chen, Yongming Fitzmeyer, Felicia Harris, Owen M Fiore, Louis D affil: Massachusetts Veterans Epidemiology Research and Information Center Cooperative Studies Coordinating Center, VA Boston Healthcare System, Jamaica Plain, Massachusetts 02130, USA sug: subj: Data Mining Electronic Health Records Natural Language Processing User-Computer Interface Algorithms Human International Classification of Diseases Neoplasms Classification Neoplasms Pathology Software ab: Reducing custom software development effort is an important goal in information retrieval (IR). This study evaluated a generalizable approach involving with no custom software or rules development. The study used documents "consistent with cancer" to evaluate system performance in the domains of colorectal (CRC), prostate (PC), and lung (LC) cancer. Using an end-user-supplied reference set, the automated retrieval console (ARC) iteratively calculated performance of combinations of natural language processing-derived features and supervised classification algorithms. Training and testing involved 10-fold cross-validation for three sets of 500 documents each. Performance metrics included recall, precision, and F-measure. Annotation time for five physicians was also measured. Top performing algorithms had recall, precision, and F-measure values as follows: for CRC, 0.90, 0.92, and 0.89, respectively; for PC, 0.97, 0.95, and 0.94; and for LC, 0.76, 0.80, and 0.75. In all but one case, conditional random fields outperformed maximum entropy-based classifiers. Algorithms had good performance without custom code or rules development, but performance varied by specific application. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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