Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records.
Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big data has a great potential to reduce the risk of CAD death. In this process, neural network (NN), as a powerful tool for electronic medical record (EMR)...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 59; no. 5; pp. 1111 - 1122 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
May2021
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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=150260079&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150260079 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2021 vid: 59 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150260079 149956689 150260079 NLM33893606 10.1007/s11517-021-02359-1 NLM33893606 150260079 ppf: 1111 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Contextual embedding bootstrapped neural network for medical information extraction of coronary artery disease records. aug: au: Cen, Xingxing Yuan, Junyi Pan, Changqing Tang, Qinhua Ma, Qunsheng affil: Shanghai Chest Hospital, Shanghai Jiaotong University, 241 West Huaihai Road, Shanghai, China sug: subj: Natural Language Processing Coronary Arteriosclerosis Information Retrieval ab: Coronary artery disease (CAD) is the major cause of human death worldwide. The development of new CAD early diagnosis methods based on medical big data has a great potential to reduce the risk of CAD death. In this process, neural network (NN), as a powerful tool for electronic medical record (EMR) processing, enables extract structured data accurately to unlock medical information and to further improve CAD diagnosis. However, the excessive time and labor caused by dataset's annotation is the main limitation of its application, especially on the CAD records situation with large natural language text and biomedical professional content. In this study, we present an annotation cost saving NN approach for CAD records, which is bootstrapped by deep language model with contextual embedding pre-trained on large unannotated CAD corpus. To demonstrate the feasibility and to further evaluate the performance of our approach, we performed pre-training experiment and term classification experiment, by using the unannotated and annotated CAD records, respectively. The results showed that our contextual embedding bootstrapped NN for CAD records has better performance under the condition of annotations reduction. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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