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Volumn , Issue , 2012, Pages 25-32

Towards large-scale twitter mining for drug-related adverse events

Author keywords

Big data analytic; Drug related adverse events; High performance computing; Mapreduce; Natural language processing; Public health; Twitter mining

Indexed keywords

ADVERSE EVENTS; BIG-DATA ANALYTIC; HIGH PERFORMANCE COMPUTING; MAP-REDUCE; NATURAL LANGUAGE PROCESSING;

EID: 84870431352     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2389707.2389713     Document Type: Conference Paper
Times cited : (185)

References (33)
  • 2
    • 0035752429 scopus 로고    scopus 로고
    • Effective mapping of biomedical text to the UMLS Metathesaurus: The MetaMap program
    • A. R. Aronson. Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. Proc AMIA Symp, pages 17-21, 2001.
    • (2001) Proc AMIA Symp , pp. 17-21
    • Aronson, A.R.1
  • 4
  • 5
    • 84863556111 scopus 로고    scopus 로고
    • Predicting adverse drug events from personal health messages
    • 2011
    • B. W. Chee, R. Berlin, and B. Schatz. Predicting adverse drug events from personal health messages. AMIA Annu Symp Proc, 2011:217-226, 2011.
    • (2011) AMIA Annu Symp Proc , pp. 217-226
    • Chee, B.W.1    Berlin, R.2    Schatz, B.3
  • 6
    • 34047138318 scopus 로고    scopus 로고
    • Combining svms with various feature selection strategies
    • Y. Chen and C. Lin. Combining svms with various feature selection strategies. Feature Extraction, pages 315-324, 2006.
    • (2006) Feature Extraction , pp. 315-324
    • Chen, Y.1    Lin, C.2
  • 7
    • 33745817438 scopus 로고    scopus 로고
    • Combining SVMs with various feature selection strategies
    • I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, Springer
    • Y.-W. Chen and C.-J. Lin. Combining SVMs with various feature selection strategies. In I. Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, Feature extraction, foundations and applications. Springer, 2006.
    • (2006) Feature Extraction, Foundations and Applications
    • Chen, Y.-W.1    Lin, C.-J.2
  • 8
    • 84859916041 scopus 로고    scopus 로고
    • Online social networks and smoking cessation: A scientific research agenda
    • N. K. Cobb. Online social networks and smoking cessation: a scientific research agenda. J. Med. Internet Res., 13:e119, 2011.
    • (2011) J. Med. Internet Res. , vol.13
    • Cobb, N.K.1
  • 11
    • 79956040653 scopus 로고    scopus 로고
    • Towards detecting influenza epidemics by analyzing twitter messages
    • SOMA '10, New York, NY, USA, ACM
    • A. Culotta. Towards detecting influenza epidemics by analyzing twitter messages. In Proceedings of the First Workshop on Social Media Analytics, SOMA '10, pages 115-122, New York, NY, USA, 2010. ACM.
    • (2010) Proceedings of the First Workshop on Social Media Analytics , pp. 115-122
    • Culotta, A.1
  • 12
    • 84870400649 scopus 로고    scopus 로고
    • European Medicines Agency., March
    • European Medicines Agency. Eudravigilance - pharmacovigilance in eea. http://eudravigilance.ema.europa.eu/human/index.asp, March 2012.
    • (2012) Eudravigilance - Pharmacovigilance in Eea
  • 14
    • 80053345545 scopus 로고    scopus 로고
    • Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures
    • Sep
    • S. A. Golder and M. W. Macy. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333(6051):1878-1881, Sep 2011.
    • (2011) Science , vol.333 , Issue.6051 , pp. 1878-1881
    • Golder, S.A.1    Macy, M.W.2
  • 15
    • 4944228528 scopus 로고    scopus 로고
    • Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
    • C. W. Hsu, C. C. Chang, and C. J. Lin. A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, 2003.
    • (2003) A Practical Guide to Support Vector Classification
    • Hsu, C.W.1    Chang, C.C.2    Lin, C.J.3
  • 16
    • 84856159600 scopus 로고    scopus 로고
    • Content-based prediction of temporal boundaries for events in twitter
    • A. Iyengar, T. Finin, and A. Joshi. Content-based prediction of temporal boundaries for events in twitter. In SocialCom/PASSAT, pages 186-191, 2011.
    • (2011) SocialCom/PASSAT , pp. 186-191
    • Iyengar, A.1    Finin, T.2    Joshi, A.3
  • 17
    • 0026891195 scopus 로고
    • An evaluation of interventions designed to stimulate physician reporting of adverse drug events
    • Jul
    • J. P. Juergens, S. L. Szeinbach, M. J. Janssen, T. R. Brown, and D. D. Garner. An evaluation of interventions designed to stimulate physician reporting of adverse drug events. Top Hosp Pharm Manage, 12(2):12-18, Jul 1992.
    • (1992) Top Hosp Pharm Manage , vol.12 , Issue.2 , pp. 12-18
    • Juergens, J.P.1    Szeinbach, S.L.2    Janssen, M.J.3    Brown, T.R.4    Garner, D.D.5
  • 20
    • 84888064144 scopus 로고    scopus 로고
    • Aggregating UMLS semantic types for reducing conceptual complexity
    • A. T. McCray, A. Burgun, and O. Bodenreider. Aggregating UMLS semantic types for reducing conceptual complexity. Stud Health Technol Inform, 84:216-220, 2001.
    • (2001) Stud Health Technol Inform , vol.84 , pp. 216-220
    • McCray, A.T.1    Burgun, A.2    Bodenreider, O.3
  • 21
    • 79958064110 scopus 로고    scopus 로고
    • Patient-assisted incident reporting: Including the patient in patient safety
    • Jun
    • E. A. Millman, P. J. Pronovost, M. A. Makary, and A. W. Wu. Patient-assisted incident reporting: including the patient in patient safety. J Patient Saf, 7(2):106-108, Jun 2011.
    • (2011) J Patient Saf , vol.7 , Issue.2 , pp. 106-108
    • Millman, E.A.1    Pronovost, P.J.2    Makary, M.A.3    Wu, A.W.4
  • 23
    • 85085133306 scopus 로고    scopus 로고
    • You are what you tweet : Analyzing twitter for public health
    • M. J. Paul and M. Dredze. You are what you tweet : Analyzing twitter for public health. Artificial Intelligence, 38:265-272, 2011.
    • (2011) Artificial Intelligence , vol.38 , pp. 265-272
    • Paul, M.J.1    Dredze, M.2
  • 25
    • 33748446657 scopus 로고    scopus 로고
    • Adverse event reporting in publications compared with sponsor database for cancer clinical trials
    • DOI 10.1200/JCO.2005.05.3959
    • O. Scharf and A. D. Colevas. Adverse event reporting in publications compared with sponsor database for cancer clinical trials. J. Clin. Oncol., 24(24):3933-3938, Aug 2006. (Pubitemid 46630741)
    • (2006) Journal of Clinical Oncology , vol.24 , Issue.24 , pp. 3933-3938
    • Scharf, O.1    Colevas, A.D.2
  • 27
    • 84870396916 scopus 로고    scopus 로고
    • The Apache Software Foundation., March
    • The Apache Software Foundation. Apache lucene core, March 2012.
    • (2012) Apache Lucene Core
  • 28
    • 84890668120 scopus 로고    scopus 로고
    • Predicting elections with twitter: What 140 characters reveal about political sentiment
    • W. W. Cohen and S. Gosling, editors, The AAAI Press
    • A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. In W. W. Cohen and S. Gosling, editors, ICWSM. The AAAI Press, 2010.
    • (2010) ICWSM
    • Tumasjan, A.1    Sprenger, T.O.2    Sandner, P.G.3    Welpe, I.M.4
  • 29
    • 84870398243 scopus 로고    scopus 로고
    • March
    • Twitter. Rest api resources. https://dev.twitter.com/docs/api, March 2012.
    • (2012) Rest Api Resources
  • 31
    • 84870406968 scopus 로고    scopus 로고
    • U.S. National Institutes of Health., March
    • U.S. National Institutes of Health. Clinicaltrails.gov. http://clinicaltrials.gov/, March 2012.
    • (2012) Clinicaltrails.gov.
  • 32
    • 84870427032 scopus 로고    scopus 로고
    • March 2012
    • U.S. National Library of Medicine. Current semantic types. http://www.nlm.nih.gov/research/umls/META3-current-semantic-types.html, March 2012.
    • Current Semantic Types
  • 33
    • 84870452887 scopus 로고    scopus 로고
    • A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations
    • W. Wang, K. Haerian, H. Salmasian, R. Harpaz, H. Chase, and C. Friedman. A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations. AMIA Annu Symp Proc, 2011:1464-1470, 2011.
    • (2011) AMIA Annu Symp Proc , pp. 1464-1470
    • Wang, W.1    Haerian, K.2    Salmasian, H.3    Harpaz, R.4    Chase, H.5    Friedman, C.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.