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Title:Scoping review on the multimodal classification of depression and experimental study on existing multimodal models
Authors:ID Arioz, Umut (Author)
ID Smrke, Urška (Author)
ID Plohl, Nejc (Author)
ID Mlakar, Izidor (Author)
Files:.pdf Scoping_Review_on_the_Multimodal_Cl-Arioz-2022.pdf (1,43 MB)
MD5: 52B9B3CBED363CE1CF62F65AB8A23866
 
URL https://www.mdpi.com/2075-4418/12/11/2683
 
Language:English
Work type:Article
Typology:1.02 - Review Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
FF - Faculty of Arts
Abstract:Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.
Keywords:multimodal depression classification, scoping review, real-world data, mental health
Publication status:Published
Publication version:Version of Record
Publication date:01.01.2022
Year of publishing:2022
Number of pages:26 str.
Numbering:Vol. 12, iss. 11
PID:20.500.12556/DKUM-84964-cb4adfeb-53a8-758a-38eb-8194cef86aa5 New window
UDC:159.92
ISSN on article:2075-4418
COBISS.SI-ID:128320003 New window
DOI:10.3390/diagnostics12112683 New window
Publication date in DKUM:11.08.2023
Views:529
Downloads:74
Metadata:XML DC-XML DC-RDF
Categories:Misc.
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Record is a part of a journal

Title:Diagnostics
Shortened title:Diagnostics
Publisher:MDPI AG
ISSN:2075-4418
COBISS.SI-ID:519963673 New window

Document is financed by a project

Funder:EC - European Commission
Funding programme:H2020
Project number:875406
Name:Patients-centered SurvivorShIp care plan after Cancer treatments based on Big Data and Artificial Intelligence technologies
Acronym:PERSIST

Funder:EC - European Commission
Funding programme:H2020
Project number:101016834
Name:Hospital Smart development based on AI
Acronym:HosmartAI

Funder:ARRS - Slovenian Research Agency
Project number:J5-3120
Name:Opolnomočenje starejših: Samoregulacijski mehanizmi in podpora digitalne tehnologije v doseganju višje kakovosti življenja

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:multimodalna klasifikacija, pregled, realni podatki, psihično zdravje


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