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Volumn , Issue , 2016, Pages 877-886

Adverse drug reaction classification with deep neural networks

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATIONAL LINGUISTICS; CONVOLUTION; NETWORK ARCHITECTURE; PHARMACODYNAMICS; RECURRENT NEURAL NETWORKS; SOCIAL NETWORKING (ONLINE);

EID: 85039449540     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (119)

References (42)
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    • (2009) 12th Conference on Artificial Intelligence in Medicine (AIME) , pp. 1-5
    • Friedman, C.1
  • 12
    • 84865989881 scopus 로고    scopus 로고
    • Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports
    • Harsha Gurulingappa, Abdul Mateen Rajput, Angus Roberts, Juliane Fluck, Martin Hofmann-Apitius, and Luca Toldo. 2012b. Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports. Journal of Biomedical Informatics, 45(5):885-892.
    • (2012) Journal of Biomedical Informatics , vol.45 , Issue.5 , pp. 885-892
    • Gurulingappa, H.1    Rajput, A.M.2    Roberts, A.3    Fluck, J.4    Hofmann-Apitius, M.5    Toldo, L.6
  • 19
    • 84902548120 scopus 로고    scopus 로고
    • Towards internet-age pharmacovigilance: Extracting adverse drug reactions from user posts to health-related social networks
    • Robert Leaman and Laura Wojtulewicz. 2010. Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In Proceedings of the workshop on biomedical natural language processing (BioNLP), pages 117-125.
    • (2010) Proceedings of the Workshop on Biomedical Natural Language Processing (BioNLP) , pp. 117-125
    • Leaman, R.1    Wojtulewicz, L.2
  • 21
    • 84881133007 scopus 로고    scopus 로고
    • AZDrugMiner: An information extraction system for mining patient-reported adverse drug events in online patient forums
    • Xiao Liu and Hsinchun Chen, 2013. AZDrugMiner: An Information Extraction System for Mining Patient-Reported Adverse Drug Events in Online Patient Forums. In Proceedings of the International Conference on Smart Health (ICSH), pages 134-150.
    • (2013) Proceedings of the International Conference on Smart Health (ICSH) , pp. 134-150
    • Liu, X.1    Chen, H.2
  • 23
    • 84874210162 scopus 로고    scopus 로고
    • Pattern mining for extraction of mentions of adverse drug reactions from user comments
    • Azadeh Nikfarjam and Graciela H Gonzalez. 2011. Pattern Mining for Extraction of mentions of Adverse Drug Reactions from User Comments. AMIA Annual Symposium Proceedings, 2011:1019-1026.
    • (2011) AMIA Annual Symposium Proceedings , vol.2011 , pp. 1019-1026
    • Nikfarjam, A.1    Gonzalez, G.H.2
  • 24
    • 84927943705 scopus 로고    scopus 로고
    • Pharmacovigilance from social media: Mining adverse drug reaction mentions using sequence labeling with word embedding cluster features
    • Azadeh Nikfarjam, Abeed Sarker, Karen O'Connor, Rachel Ginn, and Graciela Gonzalez. 2015. Pharmacovigilance from social media: Mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association, 22(3):671-681.
    • (2015) Journal of the American Medical Informatics Association , vol.22 , Issue.3 , pp. 671-681
    • Nikfarjam, A.1    Sarker, A.2    O'Connor, K.3    Ginn, R.4    Gonzalez, G.5
  • 26
  • 30
    • 84924285421 scopus 로고    scopus 로고
    • Portable automatic text classification for adverse drug reaction detection via multi-corpus training
    • Abeed Sarker and Graciela Gonzalez. 2015. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. Journal of Biomedical Informatics, 53:196-207.
    • (2015) Journal of Biomedical Informatics , vol.53 , pp. 196-207
    • Sarker, A.1    Gonzalez, G.2
  • 35
    • 65349157361 scopus 로고    scopus 로고
    • Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: A feasibility study
    • Xiaoyan Wang, George Hripcsak, Marianthi Markatou, and Carol Friedman. 2009. Active Computerized Pharmacovigilance Using Natural Language Processing, Statistics, and Electronic Health Records: A Feasibility Study. Journal of the American Medical Informatics Association, 16(3):328-337.
    • (2009) Journal of the American Medical Informatics Association , vol.16 , Issue.3 , pp. 328-337
    • Wang, X.1    Hripcsak, G.2    Markatou, M.3    Friedman, C.4
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    • ADRTrace: Detecting expected and unexpected adverse drug reactions from user reviews on social media sites
    • Andrew Yates and Nazli Goharian. 2013. ADRTrace: Detecting expected and unexpected adverse drug reactions from user reviews on social media sites. In European Conference on Information Retrieval (ECIR), pages 816-819.
    • (2013) European Conference on Information Retrieval (ECIR) , pp. 816-819
    • Yates, A.1    Goharian, N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.