Extracting entities with attributes in clinical text via joint deep learning.
Objective: Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extr...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 26; no. 12; pp. 1584 - 1592 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Dec2019
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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=139822575&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139822575 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: Dec2019 vid: 26 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 139822575 139822575 NLM31550346 139822575 10.1093/jamia/ocz158 NLM31550346 139822575 ppf: 1584 ppct: 8 formats: tig: atl: Extracting entities with attributes in clinical text via joint deep learning. aug: au: Shi, Xue Yi, Yingping Xiong, Ying Tang, Buzhou Chen, Qingcai Wang, Xiaolong Ji, Zongcheng Zhang, Yaoyun Xu, Hua affil: Department of Computer Science, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China sug: subj: Data Mining Methods Natural Language Processing Human Data Collection Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Short Portable Mental Status Questionnaire ab: Objective: Extracting clinical entities and their attributes is a fundamental task of natural language processing (NLP) in the medical domain. This task is typically recognized as 2 sequential subtasks in a pipeline, clinical entity or attribute recognition followed by entity-attribute relation extraction. One problem of pipeline methods is that errors from entity recognition are unavoidably passed to relation extraction. We propose a novel joint deep learning method to recognize clinical entities or attributes and extract entity-attribute relations simultaneously.Materials and Methods: The proposed method integrates 2 state-of-the-art methods for named entity recognition and relation extraction, namely bidirectional long short-term memory with conditional random field and bidirectional long short-term memory, into a unified framework. In this method, relation constraints between clinical entities and attributes and weights of the 2 subtasks are also considered simultaneously. We compare the method with other related methods (ie, pipeline methods and other joint deep learning methods) on an existing English corpus from SemEval-2015 and a newly developed Chinese corpus.Results: Our proposed method achieves the best F1 of 74.46% on entity recognition and the best F1 of 50.21% on relation extraction on the English corpus, and 89.32% and 88.13% on the Chinese corpora, respectively, which outperform the other methods on both tasks.Conclusions: The joint deep learning-based method could improve both entity recognition and relation extraction from clinical text in both English and Chinese, indicating that the approach is promising. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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