-
1
-
-
80053290412
-
Twitter catches the flu: Detecting influenza epidemics using twitter
-
Stroudsburg, PA, USA, Association for Computational Linguistics
-
E. Aramaki, S. Maskawa, and M. Morita. Twitter catches the flu: detecting influenza epidemics using twitter. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '11, pages 1568-1576, Stroudsburg, PA, USA, 2011. Association for Computational Linguistics.
-
(2011)
Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '11
, pp. 1568-1576
-
-
Aramaki, E.1
Maskawa, S.2
Morita, M.3
-
2
-
-
0035752429
-
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
-
3
-
-
0026966646
-
A training algorithm for optimal margin classifiers
-
New York, NY, USA, ACM
-
B. E. Boser, I. M. Guyon, and V. N. Vapnik. A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory, COLT '92, pages 144-152, New York, NY, USA, 1992. ACM.
-
(1992)
Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT '92
, pp. 144-152
-
-
Boser, B.E.1
Guyon, I.M.2
Vapnik, V.N.3
-
5
-
-
84863556111
-
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
-
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
-
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
-
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
-
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
-
-
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
-
-
-
13
-
-
70350236893
-
Discovering novel adverse drug events using natural language processing and mining of the electronic health record
-
Berlin, Heidelberg, Springer-Verlag
-
C. Friedman. Discovering novel adverse drug events using natural language processing and mining of the electronic health record. In Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine, AIME '09, pages 1-5, Berlin, Heidelberg, 2009. Springer-Verlag.
-
(2009)
Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine, AIME '09
, pp. 1-5
-
-
Friedman, C.1
-
14
-
-
80053345545
-
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
-
-
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
-
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
-
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
-
18
-
-
76149120425
-
A side effect resource to capture phenotypic effects of drugs
-
M. Kuhn, M. Campillos, I. Letunic, L. J. Jensen, and P. Bork. A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol., 6:343, 2010.
-
(2010)
Mol. Syst. Biol.
, vol.6
, pp. 343
-
-
Kuhn, M.1
Campillos, M.2
Letunic, I.3
Jensen, L.J.4
Bork, P.5
-
20
-
-
84888064144
-
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
-
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
-
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
-
24
-
-
79952396639
-
Identifying health-related topics on twitter: An exploration of tobacco-related tweets as a test topic
-
Berlin, Heidelberg, Springer-Verlag
-
K. W. Prier, M. S. Smith, C. Giraud-Carrier, and C. L. Hanson. Identifying health-related topics on twitter: an exploration of tobacco-related tweets as a test topic. In Proceedings of the 4th international conference on Social computing, behavioral-cultural modeling and prediction, SBP'11, pages 18-25, Berlin, Heidelberg, 2011. Springer-Verlag.
-
(2011)
Proceedings of the 4th International Conference on Social Computing, Behavioral-cultural Modeling and Prediction, SBP'11
, pp. 18-25
-
-
Prier, K.W.1
Smith, M.S.2
Giraud-Carrier, C.3
Hanson, C.L.4
-
25
-
-
33748446657
-
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
-
26
-
-
0027339440
-
The UMLS metathesaurus: Representing different views of biomedical concepts
-
P. L. Schuyler, W. T. Hole, M. S. Tuttle, and D. D. Sherertz. The UMLS Metathesaurus: representing different views of biomedical concepts. Bull Med Libr Assoc, 81:217-222, Apr 1993. (Pubitemid 23110177)
-
(1993)
Bulletin of the Medical Library Association
, vol.81
, Issue.2
, pp. 217-222
-
-
Schuyler, P.L.1
Hole, W.T.2
Tuttle, M.S.3
Sherertz, D.D.4
-
27
-
-
84870396916
-
-
The Apache Software Foundation., March
-
The Apache Software Foundation. Apache lucene core, March 2012.
-
(2012)
Apache Lucene Core
-
-
-
28
-
-
84890668120
-
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
-
-
March
-
Twitter. Rest api resources. https://dev.twitter.com/docs/api, March 2012.
-
(2012)
Rest Api Resources
-
-
-
31
-
-
84870406968
-
-
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
-
-
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
-
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
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