20.500.12556/DKUM-84964-cb4adfeb-53a8-758a-38eb-8194cef86aa5 Scoping review on the multimodal classification of depression and experimental study on existing multimodal models 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. multimodal depression classification scoping review real-world data mental health multimodalna klasifikacija pregled realni podatki psihično zdravje true true true Angleški jezik Slovenski jezik Članek v reviji 2023-08-11 14:59:43 2023-08-11 14:59:43 2024-03-14 12:24:18 0000-00-00 00:00:00 2022 0 0 26 str. iss. 11 Vol. 12 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2022-01-01 159.92 128320003 10.3390/diagnostics12112683 2075-4418 Scoping_Review_on_the_Multimodal_Cl-Arioz-2022.pdf Scoping_Review_on_the_Multimodal_Cl-Arioz-2022.pdf 1 52B9B3CBED363CE1CF62F65AB8A23866 93cd79a5f0e46e14d8cb2a1b09244bc7632e2dbcb028192b0da46e1513689dae 730ed514-3847-11ee-81dd-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=169422 https://www.mdpi.com/2075-4418/12/11/2683 0 7b326e52-3847-11ee-81dd-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=169423 Fakulteta za elektrotehniko, računalništvo in informatiko Filozofska fakulteta 0 0 0