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

Review (5) Machine Learning (1559) Digital Biomarkers and Digital Phenotyping (207)

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

Authors of this article:

Schenelle Dayna Dlima1 Author Orcid Image ;   Santosh Shevade1 Author Orcid Image ;   Sonia Rebecca Menezes1 Author Orcid Image ;   Aakash Ganju1 Author Orcid Image
Article Authors Cited by (16) Tweetations (27) Metrics

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
  7. Lee J, Kim M, Hwang S, Lee K, Park J, Shin T, Lim H, Urtnasan E, Chung M, Lee J. Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol. BMJ Open 2024;14(6):e073290 View
  8. Terhorst Y, Knauer J, Philippi P, Baumeister H. The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e51875 View
  9. Shang Z, Chauhan V, Devi K, Patil S. Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare – The Narrative Review. Journal of Multidisciplinary Healthcare 2024;Volume 17:4011 View
  10. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  11. Kasimovskaya N, Fomina E, Krivetskaya M, Diatlova E, Egorova E, Pavlov D. Determination of digital biomarkers of disease progression for digital phenotyping of patients with arterial hypertension. Vasa 2024;53(6):428 View
  12. Terhorst Y, Messner E, Opoku Asare K, Montag C, Kannen C, Baumeister H. Investigating Smartphone-Based Sensing Features for Depression Severity Prediction: Observation Study. Journal of Medical Internet Research 2025;27:e55308 View
  13. Sameh A, Rostami M, Oussalah M, Korpelainen R, Farrahi V. Digital phenotypes and digital biomarkers for health and diseases: a systematic review of machine learning approaches utilizing passive non-invasive signals collected via wearable devices and smartphones. Artificial Intelligence Review 2024;58(2) View
  14. Yokotani K, Yamamoto T, Takahashi H, Takamura M, Abe N. Sounds like gambling: detection of gambling venue visitation from sounds in gamblers’ environments using a transformer. Scientific Reports 2025;15(1) View