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

Machine Learning (1514) Anxiety and Stress Disorders (1082) Clinical Information and Decision Making (1447) Digital Biomarkers and Digital Phenotyping (198)

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 (8) 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, 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