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

Wearables and MHealth Reviews (273) Machine Learning (1465) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1400)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32344, first published .
Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review

Authors of this article:

Andreas Triantafyllidis1 Author Orcid Image ;   Haridimos Kondylakis2 Author Orcid Image ;   Dimitrios Katehakis2 Author Orcid Image ;   Angelina Kouroubali2 Author Orcid Image ;   Lefteris Koumakis2 Author Orcid Image ;   Kostas Marias2 Author Orcid Image ;   Anastasios Alexiadis1 Author Orcid Image ;   Konstantinos Votis1 Author Orcid Image ;   Dimitrios Tzovaras1 Author Orcid Image
Article Authors Cited by (25) Tweetations (5) Metrics

Journals

  1. Gonzalez-Ramirez M, Sanchez-Carrera R, Cejudo-Lopez A, Lozano-Navarrete M, Salamero Sánchez-Gabriel E, Torres-Bengoa M, Segura-Balbuena M, Sanchez-Cordero M, Barroso-Vazquez M, Perez-Barba F, Troncoso A, Garcia-Parrilla M, Cerezo A. Short-Term Pilot Study to Evaluate the Impact of Salbi Educa Nutrition App in Macronutrients Intake and Adherence to the Mediterranean Diet: Randomized Controlled Trial. Nutrients 2022;14(10):2061 View
  2. Subramanian H. A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach. Journal of Medical Internet Research 2023;25:e42743 View
  3. Ramesh A, Subbaraya D, Kumar D. AI based Dynamic Prediction Model for Mobile Health Application System. International Journal of Engineering and Advanced Technology 2023;12(3):19 View
  4. Misra D, Hauge E, Crowson C, Kitas G, Ormseth S, Karpouzas G. Atherosclerotic Cardiovascular Risk Stratification in the Rheumatic Diseases:. Rheumatic Disease Clinics of North America 2023;49(1):19 View
  5. Huang T, Huang L, Yang R, Li S, He N, Feng A, Li L, Lyu J. Machine learning models for predicting survival in patients with ampullary adenocarcinoma. Asia-Pacific Journal of Oncology Nursing 2022;9(12):100141 View
  6. Tan T, Shih J, Liu S, Alkhaleefah M, Chang Y, Gochoo M. Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch. Sensors 2023;23(6):3354 View
  7. Galavi Z, Khajouei R. Proposing an initial model for the heuristic evaluation of mHealth applications. Informatics in Medicine Unlocked 2023;41:101322 View
  8. Sobri M, Ijab M, Nayan N, Tondas A, Fipiariny S, Isnanto R, Hadiyanto , Warsito B. The Review of Technology in Monitoring the Heart Health of the Elderly. E3S Web of Conferences 2023;448:02029 View
  9. Minghui Y, Hu Y, Lu Z. How do nurses work in chronic management in the age of artificial intelligence? development and future prospects. DIGITAL HEALTH 2023;9 View
  10. Barfungpa S, Samantaray L, Sarma H. SMOTE-based adaptive coati kepler optimized hybrid deep network for predicting the survival of heart failure patients. Multimedia Tools and Applications 2024;83(24):65497 View
  11. Cai Y, Cai Y, Tang L, Wang Y, Gong M, Jing T, Li H, Li-Ling J, Hu W, Yin Z, Gong D, Zhang G. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review. BMC Medicine 2024;22(1) View
  12. Mroz T, Griffin M, Cartabuke R, Laffin L, Russo-Alvarez G, Thomas G, Smedira N, Meese T, Shost M, Habboub G, Yusuff K. Predicting hypertension control using machine learning. PLOS ONE 2024;19(3):e0299932 View
  13. Yi X, He Y, Gao S, Li M. A review of the application of deep learning in obesity: From early prediction aid to advanced management assistance. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2024;18(4):103000 View
  14. Gregg A, Lehmann L. Privacy and Consent in Mobile Health: Solutions for Balancing Benefits and Risks. Mayo Clinic Proceedings: Digital Health 2024;2(3):331 View
  15. Bouchareb Y, AlSaadi A, Zabah J, Jain A, Al-Jabri A, Phiri P, Shi J, Delanerolle G, Sirasanagandla S. Technological Advances in SPECT and SPECT/CT Imaging. Diagnostics 2024;14(13):1431 View
  16. Ortiz B, Gupta V, Kumar R, Jalin A, Cao X, Ziegenbein C, Singhal A, Tewari M, Choi S. Data Preprocessing Techniques for AI and Machine Learning Readiness: Scoping Review of Wearable Sensor Data in Cancer Care. JMIR mHealth and uHealth 2024;12:e59587 View
  17. Žlahtič B, Završnik J, Kokol P, Blažun Vošner H, Sobotkiewicz N, Antolinc Schaubach B, Kirbiš S. Trusting AI made decisions in healthcare by making them explainable. Science Progress 2024;107(3) View
  18. Ahmad Zargar A, Kumar R, Sharma A. Prediction of Different Risk Factors in Relation to Hyperlipidemia Using Framingham Risk Score and Cholesterol Risk Score in a Tertiary Care Hospital. Current Diabetes Reviews 2025;21(2) View

Books/Policy Documents

  1. Thakkar H, Sahoo P. Predictive Analytics in Cloud, Fog, and Edge Computing. View
  2. Sobri M, Ijab M, Nayan N, Tondas A. Advances in Visual Informatics. View
  3. Gouri M, Gummalla S, Muntha R, Nagarajan M, Jothikumar R, Kumar K, Susi S. Handbook of Research on Advanced Functional Materials for Orthopedic Applications. View
  4. Tualombo M, Carlosama L, Nieto B, Montenegro-Montenegro D, Villalba-Meneses F, Cadena-Morejón C, Almeida-Galárraga D, Tirado-Espín A. Communication and Applied Technologies. View
  5. Komalasari R. Federated Learning and Privacy-Preserving in Healthcare AI. View
  6. Katiyar N, Thakur H, Ghatak A, Raj M. ICT for Intelligent Systems. View