Detecting drugs and adverse events from Spanish health social media streams

Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis, Louhi 2014 at the 14th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2014

Volumn , Issue , 2014, Pages 106-115

  Segura Bedmar, Isabel ^a   Revert, Ricardo ^a   Martínez, Paloma ^a  

^a UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS;

ADVERSE EVENTS; EVENTS DETECTION; GOLD STANDARDS; RECALL AND PRECISION; SOCIAL MEDIA;

SOCIAL NETWORKING (ONLINE);

EID: 85051987559     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. Rakesh Agrawal and Ramakrishnan Srikant. 1994. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. Very Large Data Bases, 1215:487-499.
2. Alan R Aronson and Francois-Michel Lang. 2010. An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association, 17(3):229-236.
3. Alexandra Balahur. 2013. Sentiment Analysis in Social Media Texts. WASSA 2013, 120.
4. David W. Bates, R Scott Evans, Harvey Murff, Peter D. Stetson, Lisa Pizziferri and George Hripcsak. 2003. Detecting adverse events using information technology. Journal of the American Medical Informatics Association, 10(2):115-128.
5. Adrian Benton, Lye Ungar, Shawndra Hill, Sean Hennessy, Jun Mao, Annie Chung, Charles E. Leonarda and John H. Holmes. 2011. Identifying potential adverse effects using the web: A new approach to medical hypothesis generation. Journal of biomedical informatics, 44(6): 989-996.
6. Jiang Bian, Umit Topaloglu and Fan Yu. 2012. Towards large-scale twitter mining for drug-related adverse events. In Proceedings of the 2012 international workshop on Smart health and wellbeing, 25-32.
7. CA. Bond and Cynthia L. Raehl. 2006. Adverse drug reactions in United States hospitals. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 26(5):601-608.
8. Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and Psychol Meas ;20:37e46.
9. Ronald A. Fisher. 1922. On the interpretation of χ 2 from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1):87-94.
10. Flavien Bouillot, Phan N. Hai, Nicolas Béchet, Sandra Bringay, Dino Ienco, Stan Matwin, Pascal Poncelet, Mathiue Roche and Maguelonne Teisseire. 2013. How to Extract Relevant Knowledge from Tweets?. Communications in Computer and Information Science.
11. Carol Friedman. 2009. Discovering novel adverse drug events using natural language processing and mining of the electronic health record. In Artificial Intelligence in Medicine. LNAI 5651:1 -5.
12. Graciela H. Gonzalez, Matthew L Scotch and Garrick L Wallstrom. Mining Social Network Postings for Mentions of Potential Adverse Drug Reactions. HHS-NIH-NLM (9/10/2012 - 8/31/2016).
13. Harsha Gurulingappa, Abdul Mateen-‐Rajput and Luca Toldo. 2012. Extraction of potential adverse drug events from medical case reports. Journal of biomedical semantics. 3(1):15.
14. Harsha Gurulingappa, Luca Toldo, Abdul Mateen-Rajput, Jan A. Kors, Adel Taweel and Yorki Tayrouz. 2013. Automatic detection of adverse events to predict drug label changes using text and data mining techniques. Pharmacoepidemiology and drug safety, 22(11):1189-1194.
15. A Herxheimer, MR Crombag and TL Alves. 2010. Direct patient reporting of adverse drug reactions. A twelve-country survey & literature review. Health Action International (HAI). Europe. Paper Series Reference 01-2010/01.
16. Shawndra Hill, Raina Merchant and Lile Ungar. (2013). Lessons Learned About Public Health from Online Crowd Surveillance. Big Data, 1(3):160-167.
17. George Hripcsak and Adam S. Rothschild. 2005. Agreement, the F-measure, and reliability in information retrieval. J Am Med Inform Assoc.12:296e8.
18. Martin Krallinger, Florian Leitner, Obdulia Rabal, Miguel Vazquez, Julen Oyarzabal and Alfonso Valencia. 2013. Overview of the chemical compound and drug name recognition (CHEMDNER) task. In BioCreative Challenge Evaluation Worksho. 2:2-33.
19. Michael Kuhn, Monica Campillos, Ivica Letunic, Lars J. Jensen and Peer Bork. 2010. A side effect resource to capture phenotypic effects of drugs. Molecular systems biology, 6(343):1-6.
20. Robert Leaman, Laura Wojtulewicz, Ryan Sullivan, Annie Skariah, Jian Yang and Graciela Gonzalez. 2010. Towards internet-age pharmacovigilance: extracting adverse drug reactions from user posts to health-related social networks. In Proceedings of the 2010 workshop on biomedical natural language processing. 117-125. Association for Computational Linguistics.
21. Anne J. Leendertse, Antoine C. Egberts, Lennar J. Stoker, & Patricia M.L.A. van den Bemt. 2008. Frequency of and risk factors for preventable medication-related hospital admissions in the Netherlands. Archives of internal medicine, 168(17), 1890.
22. Qi Li, Louise Deleger, Todd Lingren, Haijun Zhai, Megan Kaiser, Laura Stoutenborough Anil G Jegga, Kevin B Cohen and Imre Solti. 2013. Mining FDA drug labels for medical conditions. BMC medical informatics and decision making, 13(1):53.
23. Mark McClellan. 2007. Drug Safety Reform at the FDA-Pendulum Swing or Systematic Improvement?. New England Journal of Medicine, 356(17):1700-1702.
24. Silvio Moreira, Joao Filgueiras, Bruno Martins, Francisco Couto and Mario J. Silva. 2013. REACTION: A naive machine learning approach for sentiment classification. In 2nd Joint Conference on. Lexical and Computational Semantics. 2:490-494.
25. Melanie Neunerdt, Michael Reyer and Rudolf Mathar. 2013. A POS Tagger for Social Media Texts trained on Web Comments. Polibits, 48:59-66.
26. Azadeh Nikfarjam and Graciela H. Gonzalez. 2011. Pattern mining for extraction of mentions of adverse drug reactions from user comments. In AMIA Annual Symposium Proceedings, 2011:1019-1026. American Medical Informatics Association.
27. Isabel Segura-Bedmar, Paloma Martınez and María Herrero-Zazo. 2013. SemEval-2013 Task 9: Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013). 3206(65): 341-351.
28. Cornelis S. van Der Hooft, Miriam CJM Sturkenboom, Kees van Grootheest, Herre J. Kingma and Bruno HCh Stricker. 2006. Adverse drug reaction-related hospitalisations. Drug Safety, 29(2):161-168.
29. Hamish Cunningham. 2002. GATE, a general architecture for text engineering. Computers and the Humanities, 36(2):223-254.
30. M Rawlins. 1995. Pharmacovigilance: paradise lost, regained or postponed? The William Withering Lecture 1994. Journal of the Royal College of Physicians of London, 29(1): 41-49.
31. Sunghwan Sohn, Jean-Pierre A. Kocher, Christopher G. Chute and Guergana K. Savova. 2011. Drug side effect extraction from clinical narratives of psychiatry and psychology patients. Journal of the American Medical Informatics Association, 18(Suppl 1):i144-i149.
32. Özlem Uzuner, Imre Solti and Eithon Cadag. 2010. Extracting medication information from clinical text. Journal of the American Medical Informatics Association. 17(5):514-518.
33. Rong Xu and QuanQiu Wang. 2013. Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. BMC Bioinformatics, 14(1):181.
34. Karin Wester, Anna K. Jönsson, Olav Spigset, Henrik Druid and Staffan Hägg. 2008. Incidence of fatal adverse drug reactions: a population based study. British journal of clinical pharmacology, 65(4):573-579.