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Volumn 17, Issue , 2017, Pages

Entity recognition from clinical texts via recurrent neural network

Author keywords

Clinical notes; Deep learning; Entity recognition; Recurrent neural network; Sequence labeling

Indexed keywords

EXTRACTION; HUMAN; HUMAN EXPERIMENT; KNOWLEDGE BASE; MACHINE LEARNING; MEDICAL INFORMATION; MODEL; NATURAL LANGUAGE PROCESSING; NERVOUS SYSTEM; SHORT TERM MEMORY; ARTIFICIAL NEURAL NETWORK; ELECTRONIC HEALTH RECORD; MEDICAL INFORMATICS;

EID: 85021702868     PISSN: None     EISSN: 14726947     Source Type: Journal    
DOI: 10.1186/s12911-017-0468-7     Document Type: Article
Times cited : (163)

References (50)
  • 4
    • 77955287813 scopus 로고    scopus 로고
    • An overview of MetaMap: Historical perspective and recent advances
    • 20442139 2995713
    • Aronson AR, Lang F-M. An overview of MetaMap: historical perspective and recent advances. J Am Med Inform Assoc. 2010;17:229-36.
    • (2010) J Am Med Inform Assoc , vol.17 , pp. 229-236
    • Aronson, A.R.1    Lang, F.-M.2
  • 6
    • 78149490620 scopus 로고    scopus 로고
    • Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): Architecture, component evaluation and applications
    • 20819853 2995668
    • Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J Am Med Inform Assoc. 2010;17:507-13.
    • (2010) J Am Med Inform Assoc , vol.17 , pp. 507-513
    • Savova, G.K.1    Masanz, J.J.2    Ogren, P.V.3    Zheng, J.4    Sohn, S.5    Kipper-Schuler, K.C.6    Chute, C.G.7
  • 7
    • 33748046130 scopus 로고    scopus 로고
    • Extracting principal diagnosis, co-morbidity and smoking status for asthma research: Evaluation of a natural language processing system
    • Zeng QT, Goryachev S, Weiss S, Sordo M, Murphy SN, Lazarus R. Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system. BMC Med Inform Decis Mak. 2006;6:1.
    • (2006) BMC Med Inform Decis Mak , vol.6 , pp. 1
    • Zeng, Q.T.1    Goryachev, S.2    Weiss, S.3    Sordo, M.4    Murphy, S.N.5    Lazarus, R.6
  • 8
    • 78149480799 scopus 로고    scopus 로고
    • Extracting medication information from clinical text
    • 2995677
    • Uzuner Ö, Solti I, Cadag E. Extracting medication information from clinical text. J Am Med Inform Assoc. 2010;17:514-8.
    • (2010) J Am Med Inform Assoc , vol.17 , pp. 514-518
    • Uzuner, Ö.1    Solti, I.2    Cadag, E.3
  • 10
    • 84875945878 scopus 로고    scopus 로고
    • Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
    • Tang B, Cao H, Wu Y, Jiang M, Xu H. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Med Inform Decis Mak. 2013;13:1.
    • (2013) BMC Med Inform Decis Mak , vol.13 , pp. 1
    • Tang, B.1    Cao, H.2    Wu, Y.3    Jiang, M.4    Xu, H.5
  • 11
    • 80053292637 scopus 로고    scopus 로고
    • I2b2/VA challenge on concepts, assertions, and relations in clinical text
    • Uzuner Ö, South BR, Shen S, DuVall SL. i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc. 2010;2011(18):552-6.
    • (2010) J Am Med Inform Assoc , vol.2011 , Issue.18 , pp. 552-556
    • Uzuner, Ö.1    South, B.R.2    Shen, S.3    DuVall, S.L.4
  • 12
    • 80053271549 scopus 로고    scopus 로고
    • A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
    • 21508414 3168315
    • Jiang M, Chen Y, Liu M, Rosenbloom ST, Mani S, Denny JC, Xu H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. J Am Med Inform Assoc. 2011;18:601-6.
    • (2011) J Am Med Inform Assoc , vol.18 , pp. 601-606
    • Jiang, M.1    Chen, Y.2    Liu, M.3    Rosenbloom, S.T.4    Mani, S.5    Denny, J.C.6    Xu, H.7
  • 13
    • 80053241946 scopus 로고    scopus 로고
    • Machine-learned solutions for three stages of clinical information extraction: The state of the art at i2b2 2010
    • 21565856 3168309
    • de Bruijn B, Cherry C, Kiritchenko S, Martin J, Zhu X. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010. J Am Med Inform Assoc. 2011;18:557-62.
    • (2011) J Am Med Inform Assoc , vol.18 , pp. 557-562
    • De Bruijn, B.1    Cherry, C.2    Kiritchenko, S.3    Martin, J.4    Zhu, X.5
  • 14
    • 84882744737 scopus 로고    scopus 로고
    • Evaluating temporal relations in clinical text: 2012 i2b2 challenge
    • 23564629 3756273
    • Sun W, Rumshisky A, Uzuner O. Evaluating temporal relations in clinical text: 2012 i2b2 challenge. J Am Med Inform Assoc. 2013;20:806-13.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 806-813
    • Sun, W.1    Rumshisky, A.2    Uzuner, O.3
  • 15
    • 84882800112 scopus 로고    scopus 로고
    • An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge
    • 23467472 3756267
    • Xu Y, Wang Y, Liu T, Tsujii J, Eric I, Chang C. An end-to-end system to identify temporal relation in discharge summaries: 2012 i2b2 challenge. J Am Med Inform Assoc. 2013;20:849-58.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 849-858
    • Xu, Y.1    Wang, Y.2    Liu, T.3    Tsujii, J.4    Eric, I.5    Chang, C.6
  • 16
    • 84881139675 scopus 로고    scopus 로고
    • A hybrid system for temporal information extraction from clinical text
    • 23571849 3756274
    • Tang B, Wu Y, Jiang M, Chen Y, Denny JC, Xu H. A hybrid system for temporal information extraction from clinical text. J Am Med Inform Assoc. 2013;20:828-35.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 828-835
    • Tang, B.1    Wu, Y.2    Jiang, M.3    Chen, Y.4    Denny, J.C.5    Xu, H.6
  • 17
    • 84881183249 scopus 로고    scopus 로고
    • Comprehensive temporal information detection from clinical text: Medical events, time, and TLINK identification
    • 23558168 3756269
    • Sohn S, Wagholikar KB, Li D, Jonnalagadda SR, Tao C, Elayavilli RK, Liu H. Comprehensive temporal information detection from clinical text: medical events, time, and TLINK identification. J Am Med Inform Assoc. 2013;20:836-42.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 836-842
    • Sohn, S.1    Wagholikar, K.B.2    Li, D.3    Jonnalagadda, S.R.4    Tao, C.5    Elayavilli, R.K.6    Liu, H.7
  • 18
    • 84882747799 scopus 로고    scopus 로고
    • Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives
    • 23605114 3756271
    • Kovačević A, Dehghan A, Filannino M, Keane JA, Nenadic G. Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives. J Am Med Inform Assoc. 2013;20:859-66.
    • (2013) J Am Med Inform Assoc , vol.20 , pp. 859-866
    • Kovačević, A.1    Dehghan, A.2    Filannino, M.3    Keane, J.A.4    Nenadic, G.5
  • 19
    • 84940054677 scopus 로고    scopus 로고
    • Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
    • 26225918 4989908
    • Stubbs A, Kotfila C, Uzuner O. Automated systems for the de-identification of longitudinal clinical narratives: overview of 2014 i2b2/UTHealth shared task Track 1. J Biomed Inform. 2015;58:S11-9.
    • (2015) J Biomed Inform , vol.58 , pp. S11-S19
    • Stubbs, A.1    Kotfila, C.2    Uzuner, O.3
  • 20
    • 84938717641 scopus 로고    scopus 로고
    • Automatic detection of protected health information from clinic narratives
    • 26231070 4989090
    • Yang H, Garibaldi JM. Automatic detection of protected health information from clinic narratives. J Biomed Inform. 2015;58:S30-8.
    • (2015) J Biomed Inform , vol.58 , pp. S30-S38
    • Yang, H.1    Garibaldi, J.M.2
  • 21
    • 84945535937 scopus 로고    scopus 로고
    • Automatic de-identification of electronic medical records using token-level and character-level conditional random fields
    • 26122526 4988843
    • Liu Z, Chen Y, Tang B, Wang X, Chen Q, Li H, Wang J, Deng Q, Zhu S. Automatic de-identification of electronic medical records using token-level and character-level conditional random fields. J Biomed Inform. 2015;58:S47-52.
    • (2015) J Biomed Inform , vol.58 , pp. S47-S52
    • Liu, Z.1    Chen, Y.2    Tang, B.3    Wang, X.4    Chen, Q.5    Li, H.6    Wang, J.7    Deng, Q.8    Zhu, S.9
  • 22
    • 84940726529 scopus 로고    scopus 로고
    • CRFs based de-identification of medical records
    • 26315662 4988860
    • He B, Guan Y, Cheng J, Cen K, Hua W. CRFs based de-identification of medical records. J Biomed Inform. 2015;58:S39-46.
    • (2015) J Biomed Inform , vol.58 , pp. S39-S46
    • He, B.1    Guan, Y.2    Cheng, J.3    Cen, K.4    Hua, W.5
  • 23
    • 84939864819 scopus 로고    scopus 로고
    • Combining knowledge-and data-driven methods for de-identification of clinical narratives
    • 26210359 4976126
    • Dehghan A, Kovacevic A, Karystianis G, Keane JA, Nenadic G. Combining knowledge-and data-driven methods for de-identification of clinical narratives. J Biomed Inform. 2015;58:S53-9.
    • (2015) J Biomed Inform , vol.58 , pp. S53-S59
    • Dehghan, A.1    Kovacevic, A.2    Karystianis, G.3    Keane, J.A.4    Nenadic, G.5
  • 24
    • 84886418080 scopus 로고    scopus 로고
    • Overview of the ShARe/CLEF eHealth evaluation lab 2013
    • Berlin Heidelberg: Springer
    • Suominen H, Salanterä S, Velupillai S, Chapman WW, Savova G, Elhadad N, Pradhan S, South BR, Mowery DL, Jones GJ. Overview of the ShARe/CLEF eHealth evaluation lab 2013. In International Conference of the Cross-Language Evaluation Forum for European Languages. Berlin Heidelberg: Springer; 2013:212-31.
    • (2013) International Conference of the Cross-Language Evaluation Forum for European Languages , pp. 212-231
    • Suominen, H.S.1
  • 30
    • 84959862537 scopus 로고    scopus 로고
    • Relation Classification via Convolutional Deep Neural Network
    • Zeng D, Liu K, Lai S, Zhou G, Zhao J. Relation Classification via Convolutional Deep Neural Network. In: COLING. 2014. p. 2335-44.
    • (2014) COLING , pp. 2335-2344
    • Zeng, D.1    Liu, K.2    Lai, S.3    Zhou, G.4    Zhao, J.5
  • 33
    • 85016097618 scopus 로고    scopus 로고
    • Named entity recognition with bidirectional LSTM-CNNs
    • Chiu JP, Nichols E. Named entity recognition with bidirectional LSTM-CNNs. Trans Assoc Comput Linguist. 2016;4:357-70.
    • (2016) Trans Assoc Comput Linguist , vol.4 , pp. 357-370
    • Chiu, J.P.1    Nichols, E.2
  • 36
    • 84926006932 scopus 로고    scopus 로고
    • A Unified Model for Word Sense Representation and Disambiguation
    • Doha: Citeseer
    • Chen X, Liu Z, Sun M. A Unified Model for Word Sense Representation and Disambiguation. In EMNLP. Doha: Citeseer; 2014:1025-35.
    • (2014) EMNLP , pp. 1025-1035
    • Chen, X.1    Liu, Z.2    Sun, M.3
  • 37
    • 84951272941 scopus 로고    scopus 로고
    • A Fast and Accurate Dependency Parser using Neural Networks
    • Chen D, Manning CD. A Fast and Accurate Dependency Parser using Neural Networks. In: EMNLP. 2014. p. 740-50.
    • (2014) EMNLP , pp. 740-750
    • Chen, D.1    Manning, C.D.2
  • 38
    • 84862289802 scopus 로고    scopus 로고
    • Deep Learning for Efficient Discriminative Parsing
    • Collobert R. Deep Learning for Efficient Discriminative Parsing. In: AISTATS. 2011. p. 224-32.
    • (2011) AISTATS , pp. 224-232
    • Collobert, R.1
  • 41
    • 0034293152 scopus 로고    scopus 로고
    • Learning to forget: Continual prediction with LSTM
    • 1:STN:280:DC%2BD3cvnvFCnug%3D%3D 11032042
    • Gers FA, Schmidhuber J, Cummins F. Learning to forget: continual prediction with LSTM. Neural Comput. 2000;12:2451-71.
    • (2000) Neural Comput , vol.12 , pp. 2451-2471
    • Gers, F.A.1    Schmidhuber, J.2    Cummins, F.3
  • 42
    • 0031573117 scopus 로고    scopus 로고
    • Long short-term memory
    • 1:STN:280:DyaK1c%2FhvVahsQ%3D%3D 9377276
    • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735-80.
    • (1997) Neural Comput , vol.9 , pp. 1735-1780
    • Hochreiter, S.1    Schmidhuber, J.2
  • 43
    • 84897497795 scopus 로고    scopus 로고
    • On the difficulty of training recurrent neural networks
    • Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks. ICML (3). 2013;28:1310-8.
    • (2013) ICML (3) , vol.28 , pp. 1310-1318
    • Pascanu, R.1    Mikolov, T.2    Bengio, Y.3
  • 44
    • 0028392483 scopus 로고
    • Learning long-term dependencies with gradient descent is difficult
    • 1:STN:280:DC%2BD1c7gvFansQ%3D%3D 18267787
    • Bengio Y, Simard P, Frasconi P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw. 1994;5:157-66.
    • (1994) IEEE Trans Neural Netw , vol.5 , pp. 157-166
    • Bengio, Y.1    Simard, P.2    Frasconi, P.3
  • 46
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86:2278-324.
    • (1998) Proc IEEE , vol.86 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4
  • 48
    • 84994158659 scopus 로고    scopus 로고
    • Bidirectional RNN for medical event detection in electronic health records
    • Jagannatha AN, Yu H. Bidirectional RNN for medical event detection in electronic health records. In: Proceedings of NAACL-HLT. 2016. p. 473-82.
    • (2016) Proceedings of NAACL-HLT , pp. 473-482
    • Jagannatha, A.N.1    Yu, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.