Published on in Vol 3, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39618, first published .
Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Digital Phenotyping in Health Using Machine Learning Approaches: Scoping Review

Journals

  1. Marciano L, Saboor S. Reinventing mental health care in youth through mobile approaches: Current status and future steps. Frontiers in Psychology 2023;14 View
  2. He T, Belouali A, Patricoski J, Lehmann H, Ball R, Anagnostou V, Kreimeyer K, Botsis T. Trends and opportunities in computable clinical phenotyping: A scoping review. Journal of Biomedical Informatics 2023;140:104335 View
  3. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  4. Kilshaw R, Boggins A, Everett O, Butner E, Leifker F, Baucom B. Benchmarking Mental Health Status Using Passive Sensor Data: Protocol for a Prospective Observational Study. JMIR Research Protocols 2024;13:e53857 View
  5. Simon L, Terhorst Y, Cohrdes C, Pryss R, Steinmetz L, Elhai J, Baumeister H. The predictive value of supervised machine learning models for insomnia symptoms through smartphone usage behavior. Sleep Medicine: X 2024;7:100114 View
  6. Choi H, Cho Y, Min C, Kim K, Kim E, Lee S, Kim J. Multiclassification of the symptom severity of social anxiety disorder using digital phenotypes and feature representation learning. DIGITAL HEALTH 2024;10 View