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

Methods and New Tools in Mental Health Research (497) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1782) Machine Learning (1710) New Methods (1027) Clinical Information and Decision Making (1628) Artificial Intelligence (1693) Diagnostic Tools in Mental Health (278)

Published on in Vol 9, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34333, first published .
Automatic Assessment of Emotion Dysregulation in American, French, and Tunisian Adults and New Developments in Deep Multimodal Fusion: Cross-sectional Study

Automatic Assessment of Emotion Dysregulation in American, French, and Tunisian Adults and New Developments in Deep Multimodal Fusion: Cross-sectional Study

Automatic Assessment of Emotion Dysregulation in American, French, and Tunisian Adults and New Developments in Deep Multimodal Fusion: Cross-sectional Study

Authors of this article:

Federico Parra1 Author Orcid Image ;   Yannick Benezeth1 Author Orcid Image ;   Fan Yang1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Chen Z, Kulkarni P, Galatzer-Levy I, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3(11):100602 View
  2. Nejadshamsi S, Karami V, Ghourchian N, Armanfard N, Bergman H, Grad R, Wilchesky M, Khanassov V, Vedel I, Abbasgholizadeh Rahimi S. Development and Feasibility Study of HOPE Model for Prediction of Depression Among Older Adults Using Wi-Fi-based Motion Sensor Data: Machine Learning Study. JMIR Aging 2025;8:e67715 View