Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text.
Background: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 19 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
12/5/2019 Supplement 5
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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=140156070&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140156070 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 12/5/2019 Supplement 5 vid: 19 pid: 24147 pub: BioMed Central artinfo: ui: 140156070 140156070 NLM31801529 10.1186/s12911-019-0937-2 NLM31801529 140156070 ppct: 1 formats: tig: atl: Applying a deep learning-based sequence labeling approach to detect attributes of medical concepts in clinical text. aug: au: Xu, Jun Li, Zhiheng Wei, Qiang Wu, Yonghui Xiang, Yang Lee, Hee-Jin Zhang, Yaoyun Wu, Stephen Xu, Hua affil: The University of Texas School of Biomedical Informatics, 7000 Fannin St Suite, 600, Houston, TX, USA sug: subj: Natural Language Processing Barthel Index Scales Short Portable Mental Status Questionnaire ab: Background: To detect attributes of medical concepts in clinical text, a traditional method often consists of two steps: named entity recognition of attributes and then relation classification between medical concepts and attributes. Here we present a novel solution, in which attribute detection of given concepts is converted into a sequence labeling problem, thus attribute entity recognition and relation classification are done simultaneously within one step.Methods: A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect various medical concept-attribute pairs in an efficient way. We then compared our deep learning-based sequence labeling approach with traditional two-step systems for three different attribute detection tasks: disease-modifier, medication-signature, and lab test-value.Results: Our results show that the proposed method achieved higher accuracy than the traditional methods for all three medical concept-attribute detection tasks.Conclusions: This study demonstrates the efficacy of our sequence labeling approach using Bi-LSTM-CRFs on the attribute detection task, indicating its potential to speed up practical clinical NLP applications. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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