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

Formative Evaluation of Digital Health Interventions (1607) Depression and Mood Disorders; Suicide Prevention (1072) Mobile Health in Psychiatry (351) Methods and New Tools in Mental Health Research (363)

Published on in Vol 6 , No 5 (2022) :May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37736, first published .
A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study

A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study

Authors of this article:

Soumya Choudhary 1 Author Orcid Image ;   Nikita Thomas 2 Author Orcid Image ;   Janine Ellenberger 1 Author Orcid Image ;   Girish Srinivasan 1 Author Orcid Image ;   Roy Cohen 1 Author Orcid Image
Article Authors Cited by (5) Tweetations (9) Metrics

Journals

  1. Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'Évolution Psychiatrique 2022;87(4):729 View
  2. Choudhary S, Thomas N, Alshamrani S, Srinivasan G, Ellenberger J, Nawaz U, Cohen R. A Machine Learning Approach for Continuous Mining of Nonidentifiable Smartphone Data to Create a Novel Digital Biomarker Detecting Generalized Anxiety Disorder: Prospective Cohort Study. JMIR Medical Informatics 2022;10(8):e38943 View
  3. 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
  4. Ahmed A, Ramesh J, Ganguly S, Aburukba R, Sagahyroon A, Aloul F. Investigating the Feasibility of Assessing Depression Severity and Valence-Arousal with Wearable Sensors Using Discrete Wavelet Transforms and Machine Learning. Information 2022;13(9):406 View