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Citing this Article

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Published on 27.07.15 in Vol 1, No 2 (2015): Jul-Dec

This paper is in the following e-collection/theme issue:

Works citing "Identifying Adverse Effects of HIV Drug Treatment and Associated Sentiments Using Twitter"

Cosme Adrover, Todd Bodnar, Zhuojie Huang, Amalio Telenti, Marcel Salathé

JMIR Public Health Surveill 2015 (Jul 27); 1(2):e7 

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.4488):

(note that this is only a small subset of citations)

  1. . Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
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  2. Golder S, O’Connor K, Hennessy S, Gross R, Gonzalez-Hernandez G. Assessment of Beliefs and Attitudes About Statins Posted on Twitter. JAMA Network Open 2020;3(6):e208953
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  3. Mayer KH, Agwu A, Malebranche D. Barriers to the Wider Use of Pre-exposure Prophylaxis in the United States: A Narrative Review. Advances in Therapy 2020;37(5):1778
    CrossRef
  4. Aramburu MJ, Berlanga R, Lanza I. Social Media Multidimensional Analysis for Intelligent Health Surveillance. International Journal of Environmental Research and Public Health 2020;17(7):2289
    CrossRef
  5. Chan MS, Lohmann S, Morales A, Zhai C, Ungar L, Holtgrave DR, Albarracín D. An Online Risk Index for the Cross-Sectional Prediction of New HIV Chlamydia, and Gonorrhea Diagnoses Across U.S. Counties and Across Years. AIDS and Behavior 2018;22(7):2322
    CrossRef
  6. Kim Y, Huang J, Emery S. Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection. Journal of Medical Internet Research 2016;18(2):e41
    CrossRef
  7. Bousquet C, Dahamna B, Guillemin-Lanne S, Darmoni SJ, Faviez C, Huot C, Katsahian S, Leroux V, Pereira S, Richard C, Schück S, Souvignet J, Lillo-Le Louët A, Texier N. The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process. JMIR Research Protocols 2017;6(9):e179
    CrossRef
  8. Convertino I, Ferraro S, Blandizzi C, Tuccori M. The usefulness of listening social media for pharmacovigilance purposes: a systematic review. Expert Opinion on Drug Safety 2018;17(11):1081
    CrossRef
  9. Jordan S, Hovet S, Fung I, Liang H, Fu K, Tse Z. Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response. Data 2018;4(1):6
    CrossRef
  10. Katsuki T, Mackey TK, Cuomo R. Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data. Journal of Medical Internet Research 2015;17(12):e280
    CrossRef
  11. . Digital Pharmacovigilance and Disease Surveillance: Combining Traditional and Big-Data Systems for Better Public Health. Journal of Infectious Diseases 2016;214(suppl 4):S399
    CrossRef
  12. Mackey TK, Nayyar G. A review of existing and emerging digital technologies to combat the global trade in fake medicines. Expert Opinion on Drug Safety 2017;16(5):587
    CrossRef
  13. Créquit P, Mansouri G, Benchoufi M, Vivot A, Ravaud P. Mapping of Crowdsourcing in Health: Systematic Review. Journal of Medical Internet Research 2018;20(5):e187
    CrossRef
  14. Roberts K, Boland MR, Pruinelli L, Dcruz J, Berry A, Georgsson M, Hazen R, Sarmiento RF, Backonja U, Yu K, Jiang Y, Brennan PF. Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics. Journal of the American Medical Informatics Association 2017;24(e1):e185
    CrossRef
  15. Huang R, Liu N, Nicdao MA, Mikaheal M, Baldacchino T, Albeos A, Petoumenos K, Sud K, Kim J. Emotion sharing in remote patient monitoring of patients with chronic kidney disease. Journal of the American Medical Informatics Association 2020;27(2):185
    CrossRef
  16. Ocampo JMF, Smart J, Allston A, Bhattacharjee R, Boggavarapu S, Carter S, Castel AD, Collmann J, Flynn C, Hamp A, Jordan D, Kassaye S, Kharfen M, Lum G, Pemmaraju R, Rhodes A, Stover J, Young MA. Improving HIV Surveillance Data for Public Health Action in Washington, DC: A Novel Multiorganizational Data-Sharing Method. JMIR Public Health and Surveillance 2016;2(1):e3
    CrossRef
  17. Fagherazzi G, Ravaud P. Digital diabetes: Perspectives for diabetes prevention, management and research. Diabetes & Metabolism 2019;45(4):322
    CrossRef
  18. Park H, Jung H, On J, Park SK, Kang H. Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies. Healthcare Informatics Research 2018;24(4):253
    CrossRef
  19. Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
    CrossRef
  20. Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Computers in Biology and Medicine 2020;122:103770
    CrossRef
  21. Lardon J, Bellet F, Aboukhamis R, Asfari H, Souvignet J, Jaulent M, Beyens M, Lillo-LeLouët A, Bousquet C. Evaluating Twitter as a complementary data source for pharmacovigilance. Expert Opinion on Drug Safety 2018;17(8):763
    CrossRef
  22. Radzikowski J, Stefanidis A, Jacobsen KH, Croitoru A, Crooks A, Delamater PL. The Measles Vaccination Narrative in Twitter: A Quantitative Analysis. JMIR Public Health and Surveillance 2016;2(1):e1
    CrossRef
  23. Routray R, Tetarenko N, Abu-Assal C, Mockute R, Assuncao B, Chen H, Bao S, Danysz K, Desai S, Cicirello S, Willis V, Alford SH, Krishnamurthy V, Mingle E. Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination. Drug Safety 2020;43(1):57
    CrossRef
  24. Ahne A, Orchard F, Tannier X, Perchoux C, Balkau B, Pagoto S, Harding JL, Czernichow T, Fagherazzi G. Insulin pricing and other major diabetes-related concerns in the USA: a study of 46 407 tweets between 2017 and 2019. BMJ Open Diabetes Research & Care 2020;8(1):e001190
    CrossRef
  25. Leis A, Ronzano F, Mayer MA, Furlong LI, Sanz F. Evaluating Behavioral and Linguistic Changes During Drug Treatment for Depression Using Tweets in Spanish: Pairwise Comparison Study. Journal of Medical Internet Research 2020;22(12):e20920
    CrossRef
  26. Mendhe CH, Henderson N, Srivastava G, Mago V. A Scalable Platform to Collect, Store, Visualize, and Analyze Big Data in Real Time. IEEE Transactions on Computational Social Systems 2021;8(1):260
    CrossRef
  27. van Heerden A, Young S, Park CS. Use of social media big data as a novel HIV surveillance tool in South Africa. PLOS ONE 2020;15(10):e0239304
    CrossRef
  28. Bour C, Schmitz S, Ahne A, Perchoux C, Dessenne C, Fagherazzi G. Scoping review protocol on the use of social media for health research purposes. BMJ Open 2021;11(2):e040671
    CrossRef
  29. Jarynowski A, Semenov A, Kamiński M, Belik V. Mild Adverse Events of Sputnik V Vaccine in Russia: Social Media Content Analysis of Telegram via Deep Learning. Journal of Medical Internet Research 2021;23(11):e30529
    CrossRef
  30. Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics 2022;14(2):266
    CrossRef
  31. Déguilhem A, Malaab J, Talmatkadi M, Renner S, Foulquié P, Fagherazzi G, Loussikian P, Marty T, Mebarki A, Texier N, Schuck S. Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media. JMIR Infodemiology 2022;2(2):e39849
    CrossRef
  32. Shakeri Hossein Abad Z, Butler GP, Thompson W, Lee J. Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk. Journal of Medical Internet Research 2022;24(1):e28749
    CrossRef
  33. Effenberger M, Kronbichler A, Bettac E, Grabherr F, Grander C, Adolph TE, Mayer G, Zoller H, Perco P, Tilg H. Using Infodemiology Metrics to Assess Public Interest in Liver Transplantation: Google Trends Analysis. Journal of Medical Internet Research 2021;23(8):e21656
    CrossRef
  34. . Digital epidemiology, deep phenotyping and the enduring fantasy of pathological omniscience. Big Data & Society 2022;9(1):205395172110664
    CrossRef
  35. Pathak R, Catalan-Matamoros D. Can Twitter posts serve as early indicators for potential safety signals? A retrospective analysis. International Journal of Risk & Safety in Medicine 2023;34(1):41
    CrossRef
  36. Jang M, Cha S, Kim S, Lee S, Lee KE, Shin K. Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database. Expert Opinion on Drug Safety 2023;22(7):629
    CrossRef
  37. Stemmer M, Parmet Y, Ravid G. Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(8):e29186
    CrossRef
  38. Koss J, Rheinlaender A, Truebel H, Bohnet-Joschko S. Social media mining in drug development—Fundamentals and use cases. Drug Discovery Today 2021;26(12):2871
    CrossRef
  39. Keller R, Spanu A, Puhan MA, Flahault A, Lovis C, Mütsch M, Beau-Lejdstrom R. Social media and internet search data to inform drug utilization: A systematic scoping review. Frontiers in Digital Health 2023;5
    CrossRef
  40. Young LE, Nan Y, Jang E, Stevens R. Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review. Current HIV/AIDS Reports 2023;20(6):470
    CrossRef
  41. Ijaz M, Anwar N, Safran M, Alfarhood S, Sadad T, Imran , Rana T. Domain adaptive learning for multi realm sentiment classification on big data. PLOS ONE 2024;19(4):e0297028
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.4488):

  1. Alves VM, Capuzzi SJ, Baker N, Muratov EN, Trospsha A, Hickey AJ. Approaching Complex Diseases. 2020. Chapter 4:77
    CrossRef
  2. Jiang K, Zhang D, Bernard GR. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2021. Chapter 35:468
    CrossRef