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

Machine Learning (1971)

Published on in Vol 7, No 2 (2019): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12561, first published .
Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia

Authors of this article:

Kyle Haas1 Author Orcid Image ;   Zina Ben Miled1 Author Orcid Image ;   Malika Mahoui2 Author Orcid Image
Article Authors Cited by (12) Tweetations (4) Metrics

Journals

  1. Horne W, Ben Miled Z. Making the Case for a P2P Personal Health Record. Information 2020;11(11):512 View
  2. Chang T, Bridges J, Bynum M, Jackson J, Joseph J, Fischer M, Lu B, Donneyong M. Association Between Patient‐Clinician Relationships and Adherence to Antihypertensive Medications Among Black Adults: An Observational Study Design. Journal of the American Heart Association 2021;10(14) View
  3. Miles S, Yao L, Meng W, Black C, Miled Z. Comparing PSO-based clustering over contextual vector embeddings to modern topic modeling. Information Processing & Management 2022;59(3):102921 View
  4. Bohlmann A, Mostafa J, Kumar M. Machine Learning and Medication Adherence: Scoping Review. JMIRx Med 2021;2(4):e26993 View
  5. Kanyongo W, Ezugwu A. Feature selection and importance of predictors of non-communicable diseases medication adherence from machine learning research perspectives. Informatics in Medicine Unlocked 2023;38:101232 View
  6. Donneyong M, Bynum M, Kemavor A, Crossnohere N, Schuster A, Bridges J, Ng Q. Patient satisfaction with the quality of care received is associated with adherence to antidepressant medications. PLOS ONE 2024;19(1):e0296062 View
  7. Islam M, Oyarzun-Gonzalez X, Bose-Brill S, Donneyong M. Supplemental Nutrition Assistance Program and Adherence to Antihypertensive Medications. JAMA Network Open 2024;7(2):e2356619 View
  8. Kaur N, Gonzales, IV M, Alcaraz C, Gong J, Wells K, Barnes L, Tsaneva-Atanasova K. A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach. PLOS Digital Health 2025;4(6):e0000839 View
  9. Li C, Jiang W, Shen A, Li Y, Wu J, Tao H, Tang Y, Yue X, Hao A, Zhao Z. International expert consensus on hospital intelligent pharmacy. Intelligent Pharmacy 2025 View
  10. Rhudy C, Johnson J, Perry C, Bumgardner C, Wesley M, Fardo D, Barrett T, Talbert J. Machine learning approaches to predicting medication nonadherence: a scoping review. International Journal of Medical Informatics 2025;204:106082 View
  11. Li S, Li X, Yu K, Wu Q, Miao D, Zhu M, Yan M, Ke Y, D’Agostino D, Ning Y, Wang Z, Shang Y, Liu M, Hong C, Liu N. Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Structured Data Analysis. Health Data Science 2025;5 View

Conference Proceedings

  1. Marvin G, Alam M. 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). A Machine Learning Approach for Predicting Therapeutic Adherence to Osteoporosis Treatment View