Ad hoc classification of radiology reports.
Objective: The task of ad hoc classification is to automatically place a large number of text documents into nonstandard categories that are determined by a user. The authors examine the use of statistical information retrieval techniques for ad hoc classification of dictated mammography reports.Des...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 6; no. 5; pp. 393 - 412 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Sep1999
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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=107231421&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 107231421 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: Sep1999 vid: 6 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 107231421 107231421 NLM10495099 1999080983 10.1136/jamia.1999.0060393 NLM10495099 107231421 ppf: 393 ppct: 19 formats: tig: atl: Ad hoc classification of radiology reports. aug: au: Aronow DB Fangfang F Croft WB Aronow, D B Fangfang, F Croft, W B affil: Center for Intelligent Information Retrieval, University of Massachusetts, Amherst 01003, USA sug: subj: Artificial Intelligence Medical Records Classification Radiography Funding Source Information Retrieval Abstracting and Indexing Reports Human ab: Objective: The task of ad hoc classification is to automatically place a large number of text documents into nonstandard categories that are determined by a user. The authors examine the use of statistical information retrieval techniques for ad hoc classification of dictated mammography reports.Design: The authors' approach is the automated generation of a classification algorithm based on positive and negative evidence that is extracted from relevance-judged documents. Test documents are sorted into three conceptual bins: membership in a user-defined class, exclusion from the user-defined class, and uncertain. Documentation of absent findings through the use of negation and conjunction, a hallmark of interpretive test results, is managed by expansion and tokenization of these phrases.Measurements: Classifier performance is evaluated using a single measure, the F measure, which provides a weighted combination of recall and precision of document sorting into true positive and true negative bins.Results: Single terms are the most effective text feature in the classification profile, with some improvement provided by the addition of pairs of unordered terms to the profile. Excessive iterations of automated classifier enhancement degrade performance because of overtraining. Performance is best when the proportions of relevant and irrelevant documents in the training collection are close to equal. Special handling of negation phrases improves performance when the number of terms in the classification profile is limited.Conclusions: The ad hoc classifier system is a promising approach for the classification of large collections of medical documents. NegExpander can distinguish positive evidence from negative evidence when the negative evidence plays an important role in the classification. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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