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

Formative Evaluation of Digital Health Interventions (2495) Mobile Health (mhealth) (2987) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1568) Depression and Mood Disorders; Suicide Prevention (1393) Mental Health Issues in Adolescence (457)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/45991, first published .
Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

Prediction of Diagnosis and Treatment Response in Adolescents With Depression by Using a Smartphone App and Deep Learning Approaches: Usability Study

Authors of this article:

Jae Sung Kim1 Author Orcid Image ;   Bohyun Wang2 Author Orcid Image ;   Meelim Kim3, 4, 5 Author Orcid Image ;   Jung Lee6 Author Orcid Image ;   Hyungjun Kim7 Author Orcid Image ;   Danyeul Roh7 Author Orcid Image ;   Kyung Hwa Lee8 Author Orcid Image ;   Soon-Beom Hong8 Author Orcid Image ;   Joon Shik Lim2 Author Orcid Image ;   Jae-Won Kim8 Author Orcid Image ;   Neal Ryan9 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

Journals

  1. Vignapiano A, Monaco F, Pagano C, Piacente M, Farina F, Petrillo G, Sica R, Marenna A, Shin J, Solmi M, Corrivetti G. A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms. Frontiers in Psychiatry 2023;14 View
  2. Leaning I, Ikani N, Savage H, Leow A, Beckmann C, Ruhé H, Marquand A. From smartphone data to clinically relevant predictions: A systematic review of digital phenotyping methods in depression. Neuroscience & Biobehavioral Reviews 2024;158:105541 View
  3. 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
  4. Zhou Y, Chen X, Gu R, Xiang Y, Hajcak G, Wang G. Personalized identification and intervention of depression in adolescents: A tertiary-level framework. Science Bulletin 2024;69(7):867 View
  5. 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 View
  6. Ng M, Frederick J, Fisher A, Allen N, Pettit J, McMakin D. Identifying Person-Specific Drivers of Depression in Adolescents: Protocol for a Smartphone-Based Ecological Momentary Assessment and Passive Sensing Study. JMIR Research Protocols 2024;13:e43931 View
  7. 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
  8. Ravan M, Noroozi A, Gediya H, James Basco K, Hasey G. Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo. Clinical Neurophysiology 2024;167:198 View
  9. Haley F, Andrews J, Moghaddam N. Advancements and Limitations: A Systematic Review of Remote-Based Deep Learning Predictive Algorithms for Depression. Journal of Technology in Behavioral Science 2024 View