Mining Online Social Network Data for Biomedical Research: A Comparison of Clinicians’ and Patients’ Perceptions About Amyotrophic Lateral Sclerosis Treatments

Mining Online Social Network Data for Biomedical Research: A Comparison of Clinicians’ and Patients’ Perceptions About Amyotrophic Lateral Sclerosis Treatments

Mining Online Social Network Data for Biomedical Research: A Comparison of Clinicians’ and Patients’ Perceptions About Amyotrophic Lateral Sclerosis Treatments

Journals

  1. Mullins C, Vandigo J, Zheng Z, Wicks P. Patient-Centeredness in the Design of Clinical Trials. Value in Health 2014;17(4):471 View
  2. Keller B, Labrique A, Jain K, Pekosz A, Levine O. Mind the Gap: Social Media Engagement by Public Health Researchers. Journal of Medical Internet Research 2014;16(1):e8 View
  3. Himes B, Weitzman E. Innovations in health information technologies for chronic pulmonary diseases. Respiratory Research 2016;17(1) View
  4. Sohn M, Jeong S, Kim J, Lee H. Crowdsourced healthcare knowledge creation using patients’ health experience-ontologies. Soft Computing 2017;21(18):5207 View
  5. Halpin L, Savulescu J, Talbot K, Turner M, Talman P. Improving access to medicines: empowering patients in the quest to improve treatment for rare lethal diseases. Journal of Medical Ethics 2015;41(12):987 View
  6. Krishnamurthy M, Marcinek P, Malik K, Afzal M. Representing Social Network Patient Data as Evidence-Based Knowledge to Support Decision Making in Disease Progression for Comorbidities. IEEE Access 2018;6:12951 View
  7. Pappa D, Stergioulas L. Harnessing social media data for pharmacovigilance: a review of current state of the art, challenges and future directions. International Journal of Data Science and Analytics 2019;8(2):113 View
  8. Schroeder E, Desai J, Schmittdiel J, Paolino A, Schneider J, Goodrich G, Lawrence J, Newton K, Nichols G, O'Connor P, Fitz-Randolph M, Steiner J. An Innovative Approach to Informing Research: Gathering Perspectives on Diabetes Care Challenges From an Online Patient Community. interactive Journal of Medical Research 2015;4(2):e13 View
  9. Pagnini F, Phillips D, Bosma C, Reece A, Langer E. Mindfulness, physical impairment and psychological well-being in people with amyotrophic lateral sclerosis. Psychology & Health 2015;30(5):503 View
  10. Lalezari S, Acquadro M, de Bock E, Lambert J, Simpson M. Comparing Physician and Patient Perspectives on Prophylactic Treatment with BAY 94-9027 for Severe Haemophilia A: A Post Hoc Analysis. Advances in Therapy 2020;37(6):2763 View
  11. Hartzler A, Taylor M, Park A, Griffiths T, Backonja U, McDonald D, Wahbeh S, Brown C, Pratt W. Leveraging cues from person-generated health data for peer matching in online communities. Journal of the American Medical Informatics Association 2016;23(3):496 View
  12. Hüning K, Weydt P, Hesse M, Ates G, Cuhls H, Radbruch L. Pharmakologische und nicht pharmakologische Symptomtherapie bei amyotropher Lateralsklerose. Nervenheilkunde 2020;39(12):791 View
  13. Samara V, Jerant P, Gibson S, Bromberg M. Bowel, bladder, and sudomotor symptoms in ALS patients. Journal of the Neurological Sciences 2021;427:117543 View
  14. Miller E, Woodward A, Flinchum G, Young J, Tabor H, Halley M. Opportunities and pitfalls of social media research in rare genetic diseases: a systematic review. Genetics in Medicine 2021 View

Books/Policy Documents

  1. Peleg M, Leung T, Desai M, Dumontier M. Artificial Intelligence in Medicine. View
  2. Heyen N, Dickel S. Personal Health Science. View
  3. Parish M, Yellowlees P. Mental Health Informatics. View