Published on in Vol 21, No 11 (2019): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14285, first published .
Using Social Media to Uncover Treatment Experiences and Decisions in Patients With Acute Myeloid Leukemia or Myelodysplastic Syndrome Who Are Ineligible for Intensive Chemotherapy: Patient-Centric Qualitative Data Analysis

Using Social Media to Uncover Treatment Experiences and Decisions in Patients With Acute Myeloid Leukemia or Myelodysplastic Syndrome Who Are Ineligible for Intensive Chemotherapy: Patient-Centric Qualitative Data Analysis

Using Social Media to Uncover Treatment Experiences and Decisions in Patients With Acute Myeloid Leukemia or Myelodysplastic Syndrome Who Are Ineligible for Intensive Chemotherapy: Patient-Centric Qualitative Data Analysis

Journals

  1. Audeh B, Bellet F, Beyens M, Lillo-Le Louët A, Bousquet C. Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project. Drug Safety 2020;43(9):835 View
  2. Lin T, Pagano L. The important role of intensive induction chemotherapy in the treatment of acute myeloid leukemia. Expert Review of Hematology 2021;14(3):303 View
  3. Cheng W, Satija A, Cheung H, Hill K, Wert T, Laliberté F, Lefebvre P. Persistence to hypomethylating agents and clinical and economic outcomes among patients with myelodysplastic syndromes. Hematology 2021;26(1):261 View
  4. Matsuda S, Ohtomo T, Tomizawa S, Miyano Y, Mogi M, Kuriki H, Nakayama T, Watanabe S. Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus. JMIR Public Health and Surveillance 2021;7(6):e29238 View
  5. Reuter K, Angyan P, Le N, Buchanan T. Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study. JMIR Formative Research 2021;5(11):e29958 View
  6. Sekeres M, Schuster M, Joris M, Krauter J, Maertens J, Breems D, Gyan E, Kovacsovics T, Verma A, Vyas P, Wang E, Ching K, O’Brien T, Gallo Stampino C, Ma W, Kudla A, Chan G, Zeidan A. A phase 1b study of glasdegib + azacitidine in patients with untreated acute myeloid leukemia and higher-risk myelodysplastic syndromes. Annals of Hematology 2022;101(8):1689 View
  7. Decroisette C, Corre R, Greenwood W, Chartier D, Amsellem N, Lefebvre-Nare F, Guéroult-Accolas L, Schott R. Analyse sémantique de conversations sur le web portant sur le cancer du poumon : étude Web Ethnography-Lung. Bulletin du Cancer 2022;109(7-8):805 View
  8. Cormican O, Dowling M. Providing Care to People Living with a Chronic Hematological Malignancy: A Qualitative Evidence Synthesis of Informal Carers’ Experiences. Seminars in Oncology Nursing 2022;38(6):151338 View
  9. Schmidt A, Rodriguez-Esteban R, Gottowik J, Leddin M. Applications of quantitative social media listening to patient-centric drug development. Drug Discovery Today 2022;27(5):1523 View
  10. Frank P, Lu M, Sasse E. Educational and Emotional Needs of Patients with Myelodysplastic Syndromes: An AI Analysis of Multi-Country Social Media. Advances in Therapy 2023;40(1):159 View
  11. Poor E, Chan Y, Iadonisi K, Tan K, Leak Bryant A. Exploring Experiences of Bereaved Caregivers of Older Adult Patients With Acute Myeloid Leukemia. Clinical Journal of Oncology Nursing 2022;26(2):135 View
  12. Song J, Cui Y, Song J, Lee C, Wu M, Chen H. Evaluation of the Needs and Experiences of Patients with Hypertriglyceridemia: Social Media Listening Infosurveillance Study. Journal of Medical Internet Research 2023;25:e44610 View

Books/Policy Documents

  1. Mejova Y. Handbook of Computational Social Science for Policy. View