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Volumn , Issue , 2016, Pages 856-865

Structured prediction models for RNN based sequence labeling in clinical text

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS; RANDOM PROCESSES;

EID: 85072823095     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d16-1082     Document Type: Conference Paper
Times cited : (203)

References (22)
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    • John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121-2159.
    • (2011) The Journal of Machine Learning Research , vol.12 , pp. 2121-2159
    • Duchi, J.1    Hazan, E.2    Singer, Y.3
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    • 84994158659 scopus 로고    scopus 로고
    • Bidirectional rnn for medical event detection in electronic health records
    • Abhyuday Jagannatha and Hong Yu. 2016. Bidirectional rnn for medical event detection in electronic health records. In Proceedings of NAACL-HLT, pages 473-482.
    • (2016) Proceedings of NAACL-HLT , pp. 473-482
    • Jagannatha, A.1    Yu, H.2
  • 14
    • 85121365374 scopus 로고    scopus 로고
    • Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons
    • Association for Computational Linguistics
    • Andrew McCallum and Wei Li. 2003. Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4, pages 188-191. Association for Computational Linguistics.
    • (2003) Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003-Volume , vol.4 , pp. 188-191
    • McCallum, A.1    Li, W.2
  • 16
    • 84968813824 scopus 로고    scopus 로고
    • Deep patient: An unsupervised representation to predict the future of patients from the electronic health records
    • Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley. 2016. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6:26094.
    • (2016) Scientific Reports , vol.6 , pp. 26094
    • Miotto, R.1    Li, L.2    Kidd, B.A.3    Dudley, J.T.4
  • 17
    • 84929501877 scopus 로고    scopus 로고
    • A novel method of adverse event detection can accurately identify venous thromboembolisms (vtes) from narrative electronic health record data
    • Christian M Rochefort, Aman D Verma, Tewodros Eguale, Todd C Lee, and David L Buckeridge. 2015. A novel method of adverse event detection can accurately identify venous thromboembolisms (vtes) from narrative electronic health record data. Journal of the American Medical Informatics Association, 22(1):155-165.
    • (2015) Journal of the American Medical Informatics Association , vol.22 , Issue.1 , pp. 155-165
    • Rochefort, C.M.1    Verma, A.D.2    Eguale, T.3    Lee, T.C.4    Buckeridge, D.L.5
  • 22


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.