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

Machine Learning (1688) Anxiety and Stress Disorders (1160) Clinical Information and Decision Making (1604) Digital Biomarkers and Digital Phenotyping (223)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38943, first published .
A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study

A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study

A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study

Authors of this article:

Soumya Choudhary1 Author Orcid Image ;   Nikita Thomas2 Author Orcid Image ;   Sultan Alshamrani2 Author Orcid Image ;   Girish Srinivasan2 Author Orcid Image ;   Janine Ellenberger1 Author Orcid Image ;   Usman Nawaz2 Author Orcid Image ;   Roy Cohen1 Author Orcid Image
Article Authors Cited by (13) Tweetations (7) Metrics

Journals

  1. Choudhary S, Srinivasan G. The Importance of Using Binary Classification Models in Predicting Depression from a Machine Learning Perspective. Digital Medicine and Healthcare Technology 2022;2022:1 View
  2. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  3. Rochotte J, Sanap A, Silenzio V, Singh V. Predicting anxiety using Google and Youtube digital traces. Emerging Trends in Drugs, Addictions, and Health 2024;4:100145 View
  4. 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
  5. Liu W, Zhou B, Li G, Luo X. Enhanced diagnostics for generalized anxiety disorder: leveraging differential channel and functional connectivity features based on frontal EEG signals. Scientific Reports 2024;14(1) View
  6. Mulinari S. Aligning digital biomarker definitions in psychiatry with the National Institute of Mental Health Research Domain Criteria framework. NPP—Digital Psychiatry and Neuroscience 2024;2(1) View
  7. Smrke U, Mlakar I, Rehberger A, Žužek L, Plohl N. Decoding anxiety: A scoping review of observable cues. DIGITAL HEALTH 2024;10 View
  8. Todd E, Orr R, Gamage E, West E, Jabeen T, McGuinness A, George V, Phuong-Nguyen K, Voglsanger L, Jennings L, Radovic L, Angwenyi L, Taylor S, Khosravi A, Jacka F, Dawson S. Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review. Computers in Biology and Medicine 2025;185:109521 View
  9. Jean T, Guay Hottin R, Orban P, Al-Jumeily OBE D. Forecasting mental states in schizophrenia using digital phenotyping data. PLOS Digital Health 2025;4(2):e0000734 View
  10. Heckler W, Feijó L, de Carvalho J, Barbosa J. Digital phenotyping for mental health based on data analytics: A systematic literature review. Artificial Intelligence in Medicine 2025;163:103094 View
  11. Nagpal R, Singh S, Moudgil A. A Deep Learning-Based Technique for Detection of Generalized Anxiety Disorder using CNN and ResNet-like Approach. Arabian Journal for Science and Engineering 2025 View
  12. Lotfi F, Lotfi A, Lotfi M, Bjelica A, Bogdanović Z. Enhancing smart healthcare with female students’ stress and anxiety detection using machine learning. Psychology, Health & Medicine 2025:1 View

Books/Policy Documents

  1. Hinrichs M, Wang J, Roe C, Johnston E. Participatory Artificial Intelligence in Public Social Services. View