Published on in Vol 24, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34669, first published .
High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study

Journals

  1. Raza M, Venkatesh K, Kvedar J. Intelligent risk prediction in public health using wearable device data. npj Digital Medicine 2022;5(1) View
  2. Chen S, Loguercio S, Chen K, Lee S, Park J, Liu S, Sadaei H, Torkamani A. Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease. Current Cardiovascular Risk Reports 2023;17(12):215 View
  3. Ojanen P, Kertész C, Morales E, Rai P, Annala K, Knight A, Peltola J. Automatic classification of hyperkinetic, tonic, and tonic-clonic seizures using unsupervised clustering of video signals. Frontiers in Neurology 2023;14 View
  4. Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, Gopalakrishnan K, Shinde R, Rajagopal A, Samaddar P, Damani D, Shivaram S, Dey S, Mitra D, Roy S, Kulkarni K, Arunachalam S. Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives. Sensors 2023;23(12):5744 View