Expanding a radiology lexicon using contextual patterns in radiology reports.
Objective: Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 25; no. 6; pp. 679 - 686 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jun2018
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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=129696248&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 129696248 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: Jun2018 vid: 25 iid: 6 pid: 622 pub: Oxford University Press / USA artinfo: ui: 129696248 129696248 NLM29329435 129696248 10.1093/jamia/ocx152 NLM29329435 129696248 ppf: 679 ppct: 7 formats: tig: atl: Expanding a radiology lexicon using contextual patterns in radiology reports. aug: au: Percha, Bethany Yuhao Zhang Bozkurt, Selen Rubin, Daniel Altman, Russ B. Langlotz, Curtis P. Zhang, Yuhao affil: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA sug: subj: Semantics Specialties, Medical Classification Natural Language Processing Data Mining Methods Vocabulary, Controlled Algorithms Radiology Information Systems Resource Databases Human Software Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales ab: Objective: Distributional semantics algorithms, which learn vector space representations of words and phrases from large corpora, identify related terms based on contextual usage patterns. We hypothesize that distributional semantics can speed up lexicon expansion in a clinical domain, radiology, by unearthing synonyms from the corpus.Materials and Methods: We apply word2vec, a distributional semantics software package, to the text of radiology notes to identify synonyms for RadLex, a structured lexicon of radiology terms. We stratify performance by term category, term frequency, number of tokens in the term, vector magnitude, and the context window used in vector building.Results: Ranking candidates based on distributional similarity to a target term results in high curation efficiency: on a ranked list of 775 249 terms, >50% of synonyms occurred within the first 25 terms. Synonyms are easier to find if the target term is a phrase rather than a single word, if it occurs at least 100× in the corpus, and if its vector magnitude is between 4 and 5. Some RadLex categories, such as anatomical substances, are easier to identify synonyms for than others.Discussion: The unstructured text of clinical notes contains a wealth of information about human diseases and treatment patterns. However, searching and retrieving information from clinical notes often suffer due to variations in how similar concepts are described in the text. Biomedical lexicons address this challenge, but are expensive to produce and maintain. Distributional semantics algorithms can assist lexicon curation, saving researchers time and money. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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