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

Digital Biomarkers and Digital Phenotyping (191) Depression and Mood Disorders; Suicide Prevention (1363) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1399) Machine Learning (1465) Wearable Devices and Sensors (888) Diagnostic Tools in Mental Health (242) Methods and New Tools in Mental Health Research (454) mHealth for Data Collection and Research (955) Mobile Health in Psychiatry (426) New Methods (944)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24365, first published .
Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Tracking and Monitoring Mood Stability of Patients With Major Depressive Disorder by Machine Learning Models Using Passive Digital Data: Prospective Naturalistic Multicenter Study

Authors of this article:

Ran Bai1, 2 Author Orcid Image ;   Le Xiao3 Author Orcid Image ;   Yu Guo4 Author Orcid Image ;   Xuequan Zhu3 Author Orcid Image ;   Nanxi Li3 Author Orcid Image ;   Yashen Wang2 Author Orcid Image ;   Qinqin Chen2 Author Orcid Image ;   Lei Feng3 Author Orcid Image ;   Yinghua Wang2 Author Orcid Image ;   Xiangyi Yu2 Author Orcid Image ;   Chunxue Wang5 Author Orcid Image ;   Yongdong Hu6 Author Orcid Image ;   Zhandong Liu7 Author Orcid Image ;   Haiyong Xie1, 2 Author Orcid Image ;   Gang Wang1, 3 Author Orcid Image
Article Authors Cited by (29) Tweetations (3) Metrics

Journals

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  17. El Dahr Y, Perquier F, Moloney M, Woo G, Dobrin-De Grace R, Carvalho D, Addario N, Cameron E, Roos L, Szatmari P, Aitken M. Feasibility of Using Research Electronic Data Capture (REDCap) to Collect Daily Experiences of Parent-Child Dyads: Ecological Momentary Assessment Study. JMIR Formative Research 2023;7:e42916 View
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  19. 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
  20. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
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  23. Yu X, Cheng P, Yang Z, Fan H, Wang Q, Xu J, Zhu H, Gao Q. A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study. Journal of Psychiatric Research 2024;177:420 View
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Books/Policy Documents

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