Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF.
Background: Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity r...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 19; no. 3 |
|---|---|
| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
4/4/2019 Supplement 3
|
| 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=135714582&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135714582 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 4/4/2019 Supplement 3 vid: 19 iid: 3 pid: 24147 pub: BioMed Central artinfo: ui: 135714582 135714582 NLM30943972 135714582 10.1186/s12911-019-0787-y NLM30943972 135714582 ppct: 1 formats: tig: atl: Entity recognition in Chinese clinical text using attention-based CNN-LSTM-CRF. aug: au: Tang, Buzhou Wang, Xiaolong Yan, Jun Chen, Qingcai affil: Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology, (Shenzhen), 518055, Shenzhen, China sug: ab: Background: Clinical entity recognition as a fundamental task of clinical text processing has been attracted a great deal of attention during the last decade. However, most studies focus on clinical text in English rather than other languages. Recently, a few researchers have began to study entity recognition in Chinese clinical text.Methods: In this paper, a novel deep neural network, called attention-based CNN-LSTM-CRF, is proposed to recognize entities in Chinese clinical text. Attention-based CNN-LSTM-CRF is an extension of LSTM-CRF by introducing a CNN (convolutional neural network) layer after the input layer to capture local context information of words of interest and an attention layer before the CRF layer to select relevant words in the same sentence.Results: In order to evaluate the proposed method, we compare it with other two currently popular methods, CRF (conditional random field) and LSTM-CRF, on two benchmark datasets. One of the datasets is publically available and only contains contiguous clinical entities, and the other one is constructed by us and contains contiguous and discontiguous clinical entities. Experimental results show that attention-based CNN-LSTM-CRF outperforms CRF and LSTM-CRF.Conclusions: CNN and attention mechanism are individually beneficial to LSTM-CRF-based Chinese clinical entity recognition system, no matter whether contiguous clinical entities are considered. The conribution of attention mechanism is greater than CNN. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|