Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification.
Objective: This paper describes the approaches the authors developed while participating in the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate assertions on concepts and relations between concepts.Design: The authors'approaches rely on both rule-based and machine-learn...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 18; no. 5; pp. 588 - 594 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2011
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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=104577241&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104577241 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: Sep2011 vid: 18 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104577241 NLM21597105 2011241439 10.1136/amiajnl-2011-000154 NLM21597105 PMC3168313 104577241 ppf: 588 ppct: 6 formats: tig: atl: Hybrid methods for improving information access in clinical documents: concept, assertion, and relation identification. aug: au: Minard AL Ligozat AL Ben Abacha A Bernhard D Cartoni B Deléger L Grau B Rosset S Zweigenbaum P Grouin C Minard, Anne-Lyse Ligozat, Anne-Laure Ben Abacha, Asma Bernhard, Delphine Cartoni, Bruno Deléger, Louise Grau, Brigitte Rosset, Sophie Zweigenbaum, Pierre Grouin, Cyril affil: LIMSI-CNRS, Orsay Cedex, France sug: subj: Data Mining Decision Support Systems, Clinical Electronic Health Records Natural Language Processing Algorithms Expert Systems Semantics Unified Medical Language System ab: Objective: This paper describes the approaches the authors developed while participating in the i2b2/VA 2010 challenge to automatically extract medical concepts and annotate assertions on concepts and relations between concepts.Design: The authors'approaches rely on both rule-based and machine-learning methods. Natural language processing is used to extract features from the input texts; these features are then used in the authors' machine-learning approaches. The authors used Conditional Random Fields for concept extraction, and Support Vector Machines for assertion and relation annotation. Depending on the task, the authors tested various combinations of rule-based and machine-learning methods.Results: The authors'assertion annotation system obtained an F-measure of 0.931, ranking fifth out of 21 participants at the i2b2/VA 2010 challenge. The authors' relation annotation system ranked third out of 16 participants with a 0.709 F-measure. The 0.773 F-measure the authors obtained on concept extraction did not make it to the top 10.Conclusion: On the one hand, the authors confirm that the use of only machine-learning methods is highly dependent on the annotated training data, and thus obtained better results for well-represented classes. On the other hand, the use of only a rule-based method was not sufficient to deal with new types of data. Finally, the use of hybrid approaches combining machine-learning and rule-based approaches yielded higher scores. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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