Maintenance Notice

Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.

Who will be affected?

Citing this Article

Right click to copy or hit: ctrl+c (cmd+c on mac)

Published on 05.04.19 in Vol 21, No 4 (2019): April

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

Works citing "Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature"

According to Crossref, the following articles are citing this article (DOI 10.2196/12286):

(note that this is only a small subset of citations)

  1. , , , , . The Big Picture on the “AI Turn” for Digital Health: The Internet of Things and Cyber-Physical Systems. OMICS: A Journal of Integrative Biology 2019;23(6):308
    CrossRef
  2. Cresswell K, Callaghan M, Khan S, Sheikh Z, Mozaffar H, Sheikh A. Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review. Health Informatics Journal 2020;26(3):2138
    CrossRef
  3. Martínez-Agüero S, Mora-Jiménez I, Lérida-García J, Álvarez-Rodríguez J, Soguero-Ruiz C. Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit. Entropy 2019;21(6):603
    CrossRef
  4. Dimeglio C, Becouarn G, Topart P, Bodin R, Buisson JC, Ritz P. Weight Loss Trajectories After Bariatric Surgery for Obesity: Mathematical Model and Proof-of-Concept Study. JMIR Medical Informatics 2020;8(3):e13672
    CrossRef
  5. Li M, Zhang J, Zou Y, Wang F, Chen B, Guan L, Wu Y. Models for the solubility calculation of a CO2/polymer system: A review. Materials Today Communications 2020;25:101277
    CrossRef
  6. Triantafyllidis A, Kondylakis H, Votis K, Tzovaras D, Maglaveras N, Rahimi K. Features, outcomes, and challenges in mobile health interventions for patients living with chronic diseases: A review of systematic reviews. International Journal of Medical Informatics 2019;132:103984
    CrossRef
  7. Or CK, Liu K, So MKP, Cheung B, Yam LYC, Tiwari A, Lau YFE, Lau T, Hui PSG, Cheng HC, Tan J, Cheung MT. Improving Self-Care in Patients With Coexisting Type 2 Diabetes and Hypertension by Technological Surrogate Nursing: Randomized Controlled Trial. Journal of Medical Internet Research 2020;22(3):e16769
    CrossRef
  8. Dallora AL, Kvist O, Berglund JS, Ruiz SD, Boldt M, Flodmark C, Anderberg P. Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach. JMIR Medical Informatics 2020;8(9):e18846
    CrossRef
  9. Raj Theeng Tamang M, Sharif MS, Al-Bayatti AH, Alfakeeh AS, Omar Alsayed A. A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London. Symmetry 2020;12(5):866
    CrossRef
  10. López Seguí F, Ander Egg Aguilar R, de Maeztu G, García-Altés A, García Cuyàs F, Walsh S, Sagarra Castro M, Vidal-Alaball J. Teleconsultations between Patients and Healthcare Professionals in Primary Care in Catalonia: The Evaluation of Text Classification Algorithms Using Supervised Machine Learning. International Journal of Environmental Research and Public Health 2020;17(3):1093
    CrossRef
  11. , , , , . Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health. Administration and Policy in Mental Health and Mental Health Services Research 2020;47(5):795
    CrossRef
  12. Li X, Rozendaal MC, Jansen K, Jonker C, Vermetten E. Things that help out: designing smart wearables as partners in stress management. AI & SOCIETY 2020;
    CrossRef
  13. Silva KD, Lee WK, Forbes A, Demmer RT, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. International Journal of Medical Informatics 2020;143:104268
    CrossRef
  14. Figueroa CA, DeMasi O, Hernandez-Ramos R, Aguilera A. Who Benefits Most from Adding Technology to Depression Treatment and How? An Analysis of Engagement with a Texting Adjunct for Psychotherapy. Telemedicine and e-Health 2020;
    CrossRef
  15. Moore RJ, Smith R, Liu Q. Using computational ethnography to enhance the curation of real-world data (RWD) for chronic pain and invisible disability use cases. ACM SIGACCESS Accessibility and Computing 2020;(127):1
    CrossRef
  16. Montano IH, Marques G, Alonso SG, López-Coronado M, de la Torre Díez I. Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis. Journal of Medical Systems 2020;44(9)
    CrossRef
  17. Triantafyllidis A, Polychronidou E, Alexiadis A, Rocha CL, Oliveira DN, da Silva AS, Freire AL, Macedo C, Sousa IF, Werbet E, Lillo EA, Luengo HG, Ellacuría MT, Votis K, Tzovaras D. Computerized decision support and machine learning applications for the prevention and treatment of childhood obesity: A systematic review of the literature. Artificial Intelligence in Medicine 2020;104:101844
    CrossRef
  18. Aguilera A, Figueroa CA, Hernandez-Ramos R, Sarkar U, Cemballi A, Gomez-Pathak L, Miramontes J, Yom-Tov E, Chakraborty B, Yan X, Xu J, Modiri A, Aggarwal J, Jay Williams J, Lyles CR. mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study. BMJ Open 2020;10(8):e034723
    CrossRef
  19. Górriz JM, Ramírez J, Ortíz A, Martínez-Murcia FJ, Segovia F, Suckling J, Leming M, Zhang Y, Álvarez-Sánchez JR, Bologna G, Bonomini P, Casado FE, Charte D, Charte F, Contreras R, Cuesta-Infante A, Duro RJ, Fernández-Caballero A, Fernández-Jover E, Gómez-Vilda P, Graña M, Herrera F, Iglesias R, Lekova A, de Lope J, López-Rubio E, Martínez-Tomás R, Molina-Cabello MA, Montemayor AS, Novais P, Palacios-Alonso D, Pantrigo JJ, Payne BR, de la Paz López F, Pinninghoff MA, Rincón M, Santos J, Thurnhofer-Hemsi K, Tsanas A, Varela R, Ferrández JM. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing 2020;410:237
    CrossRef
  20. D'Souza M, Van Munster CEP, Dorn JF, Dorier A, Kamm CP, Steinheimer S, Dahlke F, Uitdehaag BMJ, Kappos L, Johnson M. Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study. Journal of Medical Internet Research 2020;22(5):e16669
    CrossRef
  21. Dudchenko A, Ganzinger M, Kopanitsa G. Machine Learning Algorithms in Cardiology Domain: A Systematic Review. The Open Bioinformatics Journal 2020;13(1):25
    CrossRef
  22. Adly AS, Adly AS, Adly MS. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. Journal of Medical Internet Research 2020;22(8):e19104
    CrossRef
  23. Ferrand J, Hockensmith R, Houghton RF, Walsh-Buhi ER. Evaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis Study. Journal of Medical Internet Research 2020;22(8):e19018
    CrossRef