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

Formative Evaluation of Digital Health Interventions (2491) Depression and Mood Disorders; Suicide Prevention (1392) Mobile Health in Psychiatry (438) Methods and New Tools in Mental Health Research (463)

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 Choudhary1 Author Orcid Image ;   Nikita Thomas2 Author Orcid Image ;   Janine Ellenberger1 Author Orcid Image ;   Girish Srinivasan1 Author Orcid Image ;   Roy Cohen1 Author Orcid Image
Article Authors Cited by (11) 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
  5. Oudin A, Maatoug R, Bourla A, Ferreri F, Bonnot O, Millet B, Schoeller F, Mouchabac S, Adrien V. Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health. Journal of Medical Internet Research 2023;25:e44502 View
  6. Tapia J, Duñabeitia J. Rethinking Driving Assessment: A Hypothesis-Driven Proposal for Cognitive Evaluation. OBM Neurobiology 2023;07(04):1 View
  7. Zierer C, Behrendt C, Lepach-Engelhardt A. Digital biomarkers in depression: A systematic review and call for standardization and harmonization of feature engineering. Journal of Affective Disorders 2024;356:438 View
  8. 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
  9. Imans D, Abuhmed T, Alharbi M, El-Sappagh S. Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment. Diagnostics 2024;14(21):2385 View
  10. 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