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

Formative Evaluation of Digital Health Interventions (2808) Depression and Mood Disorders; Suicide Prevention (1514) Mobile Health in Psychiatry (478) Methods and New Tools in Mental Health Research (497)

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 (15) 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, 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
  11. Alves P, Marci C, Cohen-Stavi C, Whelan K, Boussios C. A machine learning model using clinical notes to estimate PHQ-9 symptom severity scores in depressed patients. Journal of Affective Disorders 2025;376:216 View
  12. 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
  13. Yang M, Ngai E, Hu X, Hu B, Liu J, Gelenbe E, Leung V. Digital Phenotyping and Feature Extraction on Smartphone Data for Depression Detection. Proceedings of the IEEE 2024;112(12):1773 View
  14. Zhong R, Wu X, Chen J, Fang Y. Utilizing Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: A Systematic Review (Preprint). Journal of Medical Internet Research 2025 View

Conference Proceedings

  1. Solatidehkordi Z, Ramesh J, Pasquier M, Sagahyroon A, Aloul F. 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). A Survey of Machine Learning Approaches for Detecting Depression Using Smartphone Data View