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

Machine Learning (1370) Anxiety and Stress Disorders (999) Clinical Information and Decision Making (1314) Digital Biomarkers and Digital Phenotyping (177)

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 (3) 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