A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text.
Objective: To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records.Materials and Methods: A combination of supervised, unsupervised, and rule-based methods were used. Supervised methods include conditio...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 20; no. 5; pp. 867 - 876 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2013
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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=104086801&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104086801 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: Sep2013 vid: 20 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104086801 NLM23686936 2012222035 10.1136/amiajnl-2013-001619 NLM23686936 PMC3756268 104086801 ppf: 867 ppct: 9 formats: tig: atl: A flexible framework for recognizing events, temporal expressions, and temporal relations in clinical text. aug: au: Roberts, Kirk Rink, Bryan Harabagiu, Sanda M affil: Human Language Technology Research Institute, The University of Texas at Dallas, Richardson, Texas, USA. sug: subj: Artificial Intelligence Electronic Health Records Information Retrieval Methods Natural Language Processing Human Time Research, Medical ab: Objective: To provide a natural language processing method for the automatic recognition of events, temporal expressions, and temporal relations in clinical records.Materials and Methods: A combination of supervised, unsupervised, and rule-based methods were used. Supervised methods include conditional random fields and support vector machines. A flexible automated feature selection technique was used to select the best subset of features for each supervised task. Unsupervised methods include Brown clustering on several corpora, which result in our method being considered semisupervised.Results: On the 2012 Informatics for Integrating Biology and the Bedside (i2b2) shared task data, we achieved an overall event F1-measure of 0.8045, an overall temporal expression F1-measure of 0.6154, an overall temporal link detection F1-measure of 0.5594, and an end-to-end temporal link detection F1-measure of 0.5258. The most competitive system was our event recognition method, which ranked third out of the 14 participants in the event task.Discussion: Analysis reveals the event recognition method has difficulty determining which modifiers to include/exclude in the event span. The temporal expression recognition method requires significantly more normalization rules, although many of these rules apply only to a small number of cases. Finally, the temporal relation recognition method requires more advanced medical knowledge and could be improved by separating the single discourse relation classifier into multiple, more targeted component classifiers.Conclusions: Recognizing events and temporal expressions can be achieved accurately by combining supervised and unsupervised methods, even when only minimal medical knowledge is available. Temporal normalization and temporal relation recognition, however, are far more dependent on the modeling of medical knowledge. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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