Social media mining for toxicovigilance: Automatic monitoring of prescription medication abuse from twitter

Drug Safety

Volumn 39, Issue 3, 2016, Pages 231-240

  Sarker, Abeed ^a   O’Connor, Karen ^a   Ginn, Rachel ^a   Scotch, Matthew ^a   Smith, Karen ^b   Malone, Dan ^c   Gonzalez, Graciela ^a  

^a ARIZONA STATE UNIVERSITY   (United States)

^b REGIS UNIVERSITY   (United States)

^c UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMPHETAMINE PLUS DEXAMPHETAMINE; METFORMIN; OXYCODONE; QUETIAPINE; AMPHETAMINE DERIVATIVE; PRESCRIPTION DRUG;

ANALYTICAL ERROR; ARTICLE; ATTENTION DEFICIT DISORDER; CONTROLLED STUDY; DATA MINING; DRUG CLASSIFICATION; DRUG MONITORING; DRUG OVERDOSE; DRUG RECALL; DRUG SURVEILLANCE PROGRAM; HUMAN; MACHINE LEARNING; PERSONAL EXPERIENCE; PRESCRIPTION; PRIORITY JOURNAL; SOCIAL MEDIA; SUPPORT VECTOR MACHINE; DRUG MISUSE; PROCEDURES; STATISTICS AND NUMERICAL DATA; TRENDS;

AMPHETAMINES; DATA MINING; HUMANS; OXYCODONE; PRESCRIPTION DRUG MISUSE; PRESCRIPTION DRUGS; QUETIAPINE FUMARATE; SOCIAL MEDIA;

EID: 85007061942     PISSN: 01145916     EISSN: 11791942     Source Type: Journal    
DOI: 10.1007/s40264-015-0379-4     Document Type: Article

References (50)

1. Young S. White House launches effort to combat soaring prescription drug abuse. CNN 2011. http://www.cnn.com/2011/HEALTH/04/19/drug.abuse/. Accessed 15 Nov 2015.
2. The DAWN Report. Highlights of the 2011 Drug Abuse Warning Network (DAWN) findings on drug-related emergency department visits. 2013. http://archive.samhsa.gov/data/2k13/DAWN127/sr127-DAWN-highlights.htm. Accessed 15 Nov 2015.
3. Hanson CL, Cannon B, Burton S, Giraud-Carrier C. An exploration of social circles and prescription drug abuse through twitter. J Med Internet Res. 2013;15(9):e189.
4. Centers for Disease Control and Prevention: National Vital Statistics System. Mortality data. 2015. http://www.cdc.gov/nchs/deaths.htm. Accessed 15 Nov 2015.
5. Centers for Disease Control and Prevention. Prescription drug overdose in the United States: fact sheet. 2015. http://www.cdc. gov/drugoverdose/index.html. Accessed 15 Nov 2015.
6. Chary M, Genes N, McKenzie A, Manini AF. Leveraging social networks for toxicovigilance. J Med Toxicol. 2013;9(2):184-91.
7. Birnbaum HG, White AG, Schiller M, Waldman T, Cleveland JM, Roland CL. Societal costs of prescription opioid abuse, dependence, and misuse in the United States. Pain Med. 2011;12(4):657-67.
8. Centers for Disease Control and Prevention. Prescription painkiller overdoses in the US. 2015. http://www.cdc.gov/vitalsigns/painkilleroverdoses/. Accessed 15 Nov 2015.
9. Jena AB, Goldman DP. Growing Internet use may help explain the rise in prescription drug abuse in the United States. Health Aff (Millwood). 2011;30(6):1192-9.
10. Wu LT, Pilowsky DJ, Patkar AA. Non-prescribed use of pain relievers among adolescents in the United States. Drug Alcohol Depend. 2008;94(1-3):1-11.
11. Office of National Drug Control Policy (ONDCP). Responding to America’s prescription drug abuse crisis. 2011. https://www. whitehouse.gov/sites/default/files/ondcp/issuescontent/prescription-drugs/rx_abuse_plan.pdf. Accessed 15 Nov 2015.
12. Capurro D, Cole K, Echavarría MI, Joe J, Neogi T, Turner AM. The use of social networking sites for public health practice and research: a systematic review. J Med Internet Res. 2014;16(3):e79.
13. Twitter. Company. https://about.twitter.com/company. Accessed 21 May 2015.
14. Statistic Brain Research Institute. Twitter statistics. http://www. statisticbrain.com/twitter-statistics/. Accessed 19 Nov 15.
15. Kaplam AM, Haenlein M. Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz. 2010;53(1):59-68.
16. Pang B, Lee L. Opinion mining and sentiment analysis. Found Trends® Inf Retr. 2008;2(1-2):1-135.
17. Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L. Detecting influenza epidemics using search engine query data. Nature. 2009;457(7232):1012-4.
18. Gold J, Pedrana AE, Sacks-Davis R, Hellard ME, Chang S, Howard S, et al. A systematic examination of the use of online social networking sites for sexual health promotion. BMC Public Health. 2011;11:583.
19. Freifeld CC, Brownstein CC, Menone CM, Bao W, Felice R, Kass-Hout T, et al. Digital drug safety surveillance: monitoring pharmaceutical products in Twitter. Drug Saf. 2014;37(5):343-50.
20. Sarker A, Gonzalez G. Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J Biomed Inform. 2015;53:196-207.
21. Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, et al. Utilizing social media data for pharmacovigilance: a review. J Biomed Inform. 2015;54:202-12.
22. Nikfarjam A, Sarker A, O’Connor A, Ginn R, Gonzalez G. Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. J Am Med Inform Assoc. 2015;22(3):671-81.
23. Sansone RA, Sansone LA. Is seroquel developing an illicit reputation for misuse/abuse? Psychiatry (Edgmont). 2010;7(1):13-6.
24. Pimpalkhute P, Patki A, Nikfarjam A, Gonzalez G. Phonetic spelling filter for keyword selection in drug mention mining from social media. AMIA Jt Summits Transl Sci Proc. 2014;2014:90-5.
25. Carletta J. Assessing agreement on classification tasks: the kappa statistic. Comput Linguist. 1996;22(2):249-54.
26. O’Connor K, Ginn R, Smith K, Sarker A. Toxicovigilance from social media: annotation guidelines. Arizona State University. Version 1.1. July 2015. http://diego.asu.edu/guidelines/DrugAbuseAnnotationGuideline1.1.pdf. Accessed 14 Dec 2015.
27. Porter MF. An algorithm for suffix stripping. Program. 1980;14(3):130-7.
28. Owoputi O, O’Connor B, Dyer C, Gimpel K, Schneider N, Smith NA. Improved part-of-speech tagging for online conversational text with word clusters. HLT-NAACL Proc. 2013;2013:380-90.
29. Hanson CL, Burton SH, Giraud-Carrier C, West JH, Barnes MD, Hansen B. Tweaking and tweeting: exploring Twitter for nonmedical use of a psychostimulant drug (Adderall) among college students. J Med Internet Res. 2013;15(4):e62.
30. NoSlang.com Drug Slang Translator. Drug slang dictionary. http://www.noslang.com/drugs/dictionary.php. Accessed 7 May 2015.
31. Sarker A, Molla´ D, Paris C. Automatic evidence quality prediction to support evidence-based decision making. Artif Intell Med. 2015;64(2):89-103.
32. Princeton University. WordNet: a lexical database for English. https://wordnet.princeton.edu/. Accessed 19 Nov 15.
33. Turian J, Ratinov L, Bengio Y. Word representations: a simple and general method for semi-supervised learning. ACL Proc. 2010;2010:384-94.
34. Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol. 2011;2(3):1-27.
35. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: an update. SIGKDD Explor. 2009;11(1):10-8.
36. Wolpert DHDH. Wolpert. Stacked generalization. Neural Netw. 1992;5(2):241-59.
37. National Poisoning Data System. http://www.aapcc.org/datasystem/. Accessed 16 Dec 2015
38. US Food and Drug Administration (FDA). http://www.fda.gov/. Accessed 16 Dec 2015
39. US FDA. MedWatch: the FDA safety information and adverse event reporting program. http://www.fda.gov/Safety/MedWatch/. Accessed 8 Aug 2015.
40. Schifano F, Deluca P, Baldachhino A, Peltoniemi T, Scherbaum N, Torrens M, et al. Drugs on the web; the Psychonaut 2002 EU project. Prog Neuro-Psychopharmacol Biol Psychiatry. 2006;30(4):640-6.
41. Mackey K, Liang BA, Strathdee SA. Digital social media, youth, and nonmedical use of prescription drugs: the need for reform. J Med Internet Res. 2013;15(7):e143.
42. Cameron D, Smith GA, Daniulaityte R, Sheth AP, Dave L, Chen L, et al. PREDOSE: a semantic web platform for drug abuse epidemiology using social media. J Biomed Inform. 2013;46(6):985-97.
43. McNaughton C, Coplan PM, Black RA, Weber SE, Chilcoat HD, Butler SF. Monitoring of internet forums to evaluate reactions to the introduction of reformulated oxycontin to deter abuse. J Med Internet Res. 2014;16(5):e119.
44. MacLean D, Gupta S, Lembke A, Manning C, Heer J. Forum77: an analysis of an online health forum dedicated to addiction recovery. In: Proceedings of the 18th ACM conference on Computer-Supported Cooperative Work and Social Computing. New York: ACM; 2015. pp. 1511-1526.
45. Cavazos-Rehg P, Krauss M, Grucza R, Bierut L. Characterizing the followers and tweets of a marijuana-focused Twitter handle. J Med Internet Res. 2014;16(6):e157.
46. Scott KR, Nelson L, Meisel Z, Perrone J. Opportunities for exploring and reducing prescription drug abuse through social media. J Addict Dis. 2015;34(2-3):178-84.
47. Shutler L, Nelson LS, Portelli I, Blackford C, Perrone J. Drug use in the Twittersphere: a qualitative contextual analysis of tweets about prescription drugs. J Addict Dis. 2015;34(4):303-10.
48. Coloma PM, Becker B, Sturkeboom MC, van Mulligen EM, Kors JA. Evaluating social media networks in medicines safety surveillance: two case studies. Drug Saf. 2015;38(10):921-30.
49. Han B, Cook P, Baldwin T. Lexical normalization for social media text. ACM Trans Intell Syst Technol. 2013;1(1):1-27.
50. Substance Abuse and Mental Health Services Administration. Results from the 2012 National Survey on Drug Use and Health: summary of national findings. 2013. http://www.samhsa.gov/data/sites/default/files/NSDUHresultsPDFWHTML2013/Web/NSDU Hresults2013.pdf. Accessed 15 Nov 2015.