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

Reviews in Medical Informatics (93) Wearable Devices and Sensors (903) Cardiac Risk and Cardiac Risk Calculators (47) Cardiac Self-Management (108) Cardiovascular Disease Prevention (142) Mobile Apps for Cardiology (144) Consumer Health Informatics Innovations (15) New Technologies (81) Machine Learning (1501) mHealth for Wellness, Behavior Change and Prevention (2922)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29434, first published .
Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View

Authors of this article:

Arman Naseri Jahfari1, 2 Author Orcid Image ;   David Tax1 Author Orcid Image ;   Marcel Reinders1 Author Orcid Image ;   Ivo van der Bilt2 Author Orcid Image
Article Authors Cited by (9) Tweetations (8) Metrics

Journals

  1. Huang J, Wang J, Ramsey E, Leavey G, Chico T, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. Sensors 2022;22(20):8002 View
  2. Leiner J, König S, Mouratis K, Kim I, Schmitz P, Joshi T, Schanner C, Wohlrab L, Hohenstein S, Pellissier V, Nitsche A, Kuhlen R, Hindricks G, Bollmann A. A Digital Infrastructure for Cardiovascular Patient Care Based on Mobile Health Data and Patient-Reported Outcomes: Concept Details of the Helios TeleWear Project Including Preliminary Experiences. JMIR Formative Research 2023;7:e41115 View
  3. Chidambaram S, Maheswaran Y, Patel K, Sounderajah V, Hashimoto D, Seastedt K, McGregor A, Markar S, Darzi A. Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation. Sensors 2022;22(18):6920 View
  4. Moses J, Adibi S, Angelova M, Islam S. Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review. Applied System Innovation 2022;5(3):51 View
  5. Andayeshgar B, Abdali-Mohammadi F, Sepahvand M, Almasi A, Salari N. Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads. BMC Medical Research Methodology 2024;24(1) View
  6. Foote H, Cohen-Wolkowiez M, Lindsell C, Hornik C. Applying Artificial Intelligence in Pediatric Clinical Trials: Potential Impacts and Obstacles. The Journal of Pediatric Pharmacology and Therapeutics 2024;29(3):336 View
  7. Scholte N, van Ravensberg A, Shakoor A, Boersma E, Ronner E, de Boer R, Brugts J, Bruining N, van der Boon R. A scoping review on advancements in noninvasive wearable technology for heart failure management. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Zinzuwadia A, Goldberg J, Hanson M, Wessler J. Emerging Practices in Telehealth. View