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

Infodemiology and Infoveillance (1365) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1660) Participatory Medicine & E-Patients (781) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1049)

Published on in Vol 18, No 3 (2016): March

Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research

Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research

Patterns of Treatment Switching in Multiple Sclerosis Therapies in US Patients Active on Social Media: Application of Social Media Content Analysis to Health Outcomes Research

Authors of this article:

Valéry Risson1 Author Orcid Image ;   Deepanshu Saini2 Author Orcid Image ;   Ian Bonzani3 Author Orcid Image ;   Alice Huisman1 Author Orcid Image ;   Melvin Olson1 Author Orcid Image
Article Authors Cited by (30) Tweetations (21) Metrics

Journals

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  18. Kamiński M, Łoniewski I, Misera A, Marlicz W. Heartburn-Related Internet Searches and Trends of Interest across Six Western Countries: A Four-Year Retrospective Analysis Using Google Ads Keyword Planner. International Journal of Environmental Research and Public Health 2019;16(23):4591 View
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  26. Effenberger M, Kronbichler A, Bettac E, Grabherr F, Grander C, Adolph T, 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 View
  27. Leist T, Cole M, Verma S, Keenan A, Le H. A retrospective claims analysis of fatigue in patients with multiple sclerosis on disease-modifying therapy. Multiple Sclerosis and Related Disorders 2023;78:104917 View
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