Machine Learning and Medication Adherence: Scoping Review

Journals

1. Kwan Y. Peer Review of “Machine Learning and Medication Adherence: Scoping Review”. JMIRx Med 2021;2(4):e33963 View
2. Bohlmann A, Mostafa J, Kumar M. Authors' Responses to Peer Review of “Machine Learning and Medication Adherence: Scoping Review”. JMIRx Med 2021;2(4):e33962 View
3. Kardas P. Peer Review of “Machine Learning and Medication Adherence: Scoping Review”. JMIRx Med 2021;2(4):e33965 View
4. Phan P, Abad C. The future of AI in health-care quality: defining the challenges and opportunities moving forward. International Journal for Quality in Health Care 2022;34(4) View
5. Masiero M, Spada G, Sanchini V, Munzone E, Pietrobon R, Teixeira L, Valencia M, Machiavelli A, Fragale E, Pezzolato M, Pravettoni G. A Machine Learning Model to Predict Patients’ Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial. JMIR Research Protocols 2023;12:e48852 View
6. Yadav S, Yadav S, Verma P, Ojha S, Mishra S. Artificial Intelligence: An Advanced Evolution In Forensic and Criminal Investigation. Current Forensic Science 2022;1 View
7. van Breugel M, Fehrmann R, Bügel M, Rezwan F, Holloway J, Nawijn M, Fontanella S, Custovic A, Koppelman G. Current state and prospects of artificial intelligence in allergy. Allergy 2023;78(10):2623 View
8. Ekpezu A, Wiafe I, Oinas-Kukkonen H. Predicting Adherence to Behavior Change Support Systems Using Machine Learning: Systematic Review. JMIR AI 2023;2:e46779 View
9. Georges Badr N, Khiami M, Carrubbo L, Le Dinh T. Improving access to prescription-based care through patient-centered smart pharmacy ecosystems. ITM Web of Conferences 2024;62:02003 View
10. Rodriguez D, Chen J, Viswanadham R, Lawrence K, Mann D. Leveraging Machine Learning to Develop Digital Engagement Phenotypes of Users in a Digital Diabetes Prevention Program: Evaluation Study. JMIR AI 2024;3:e47122 View
11. Masiero M, Spada G, Fragale E, Pezzolato M, Munzone E, Sanchini V, Pietrobon R, Teixeira L, Valencia M, Machiavelli A, Woloski R, Marzorati C, Pravettoni G. Adherence to oral anticancer treatments: network and sentiment analysis exploring perceived internal and external determinants in patients with metastatic breast cancer. Supportive Care in Cancer 2024;32(7) View
12. Huang E, Yang J, Liao K, Tseng W, Lee C, Gill M, Compas C, See S, Tsai F. Predicting blood–brain barrier permeability of molecules with a large language model and machine learning. Scientific Reports 2024;14(1) View
13. Dave P. Using AI to increase medication adherence. Asian Journal of Dental and Health Sciences 2024;4(2):38 View
14. Amirthalingam P, Alatawi Y, Chellamani N, Shanmuganathan M, Ali M, Alqifari S, Mani V, Dhanasekaran M, Alqahtani A, Alanazi M, Aljabri A. Sea Horse Optimization–Deep Neural Network: A Medication Adherence Monitoring System Based on Hand Gesture Recognition. Sensors 2024;24(16):5224 View
15. Liu L, Yu Z, Chen H, Gong Z, Huang X, Chen L, Fan Z, Zhang J, Yan J, Tian H, Zeng X, Chen Z, Zhang P, Zhou H. Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor. Cancer 2024 View
16. Dave P. How AI Can Revolutionize the Pharmaceutical Industry. Journal of Drug Delivery and Therapeutics 2024;14(6):179 View
17. Lu H, Alhaskawi A, Dong Y, Zou X, Zhou H, Ezzi S, Kota V, Hasan Abdulla Hasan Abdulla M, Abdalbary S. Patient Autonomy in Medical Education: Navigating Ethical Challenges in the Age of Artificial Intelligence. INQUIRY: The Journal of Health Care Organization, Provision, and Financing 2024;61 View
18. Haga S. Artificial intelligence, medications, pharmacogenomics, and ethics. Pharmacogenomics 2024:1 View