Published on in Vol 8, No 12 (2021): December
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/30439, first published
.
Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review
Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review
Journals
- Lin B, Tan Z, Mo Y, Yang X, Liu Y, Xu B. Intelligent oncology: The convergence of artificial intelligence and oncology. Journal of the National Cancer Center 2023;3(1):83 View
- Arioz U, Smrke U, Plohl N, Mlakar I. Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models. Diagnostics 2022;12(11):2683 View
- Yasin S, Othmani A, Raza I, Hussain S. Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review. Computers in Biology and Medicine 2023;159:106741 View
- 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
- Wang H, Lin H, Liu B. Research progress on the psychological burden and intervention measures in cancer patients. Frontiers in Psychiatry 2024;15 View
- Mlakar I, Arioz U, Smrke U, Plohl N, Šafran V, Rojc M. An End-to-End framework for extracting observable cues of depression from diary recordings. Expert Systems with Applications 2024;257:125025 View
- Das K, Gavade P. A review on the efficacy of artificial intelligence for managing anxiety disorders. Frontiers in Artificial Intelligence 2024;7 View
- Smrke U, Mlakar I, Rehberger A, Žužek L, Plohl N. Decoding anxiety: A scoping review of observable cues. DIGITAL HEALTH 2024;10 View