A multimodal computer-aided diagnostic system for depression relapse prediction using audiovisual cues: A proof of concept

Bibliographic Details
Title: A multimodal computer-aided diagnostic system for depression relapse prediction using audiovisual cues: A proof of concept
Authors: Alice Othmani, Assaad Oussama Zeghina
Source: Healthcare Analytics, Vol 2, Iss , Pp 100090- (2022)
Publisher Information: Elsevier, 2022.
Publication Year: 2022
Collection: LCC:Computer applications to medicine. Medical informatics
Subject Terms: Clinical depression, Clinical decision support, Deep learning, Depression relapse prediction, Biomedical video processing, Predictive analytics, Computer applications to medicine. Medical informatics, R858-859.7
More Details: Major depressive disorder (MDD), also known as depression, is a common and serious mental disorder. It is characterized by a high rate of relapse or recurrence where a person might experience depressive episodes after being depression-free. Although numerous studies are proposed in the literature for depression recognition using video, to the best of our knowledge, only one preliminary study has been proposed in the literature for the automatic identification of signs of depression relapse using audiovisual cues without user intervention. In this paper, we propose a proof of concept of a deep learning-based approach for depression recognition and depression relapse prediction using videos of clinical interviews. We propose a correlation-based anomaly detection framework and a measure of similarity to depression where depression relapse is detected when the deep audiovisual patterns of a depression-free subject become close to the deep audiovisual patterns of depressed subjects. Thus, the correlation between the audiovisual encoding of a test subject and a deep audiovisual representation of depression is computed and is used for monitoring depressed subjects and for predicting relapse after depression. Very promising results are achieved with an accuracy of 80.99% and 82.55% respectively for relapse depression prediction on the DAIC-Woz dataset.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2772-4425
Relation: http://www.sciencedirect.com/science/article/pii/S2772442522000387; https://doaj.org/toc/2772-4425
DOI: 10.1016/j.health.2022.100090
Access URL: https://doaj.org/article/be267a3923bd4e2fb98e2d9424a67f6a
Accession Number: edsdoj.be267a3923bd4e2fb98e2d9424a67f6a
Database: Directory of Open Access Journals
More Details
ISSN:27724425
DOI:10.1016/j.health.2022.100090
Published in:Healthcare Analytics
Language:English