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http://hdl.handle.net/2445/213462
Title: | Transformers in depression detection from semi-structured psychological Interviews |
Author: | Honorato López, Iker |
Keywords: | Aprenentatge automàtic Tractament del llenguatge natural (Informàtica) Depressió psíquica Treballs de fi de màster Machine learning Natural language processing (Computer science) Mental depression Master's thesis |
Issue Date: | 30-Jun-2023 |
Abstract: | The expansive adoption of Transformer models across the Machine Learning landscape is undeniable, and health is not an exception. This study undertakes a rigorous exploration of the efficacy of these novel architectures in discerning depression indicators from semi-structured psychological interviews. A key focus of this study is the extrapolation of the pre-training knowledge inherent in these models, and the comparison with traditional state-of-the-art Machine Learning models. In doing so, the thesis proposes a comprehensive framework designed to facilitate objective comparison. The study extends its inquiry into the differential performance of text and speech modalities, in isolation and combination, within the context of depression detection. Moreover, this research delves into the importance of topical relevance in the detection process, culminating in an evaluative discussion of crucial themes integral to accurate depression detection. Ultimately, this thesis contributes to the deepening understanding of the complex interplay between Transformer models, modality use, and topic importance in the realm of depression detection. |
Note: | Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2022-2023. Tutor: Jordi Vitrià i Marca, Javi Jiménez i Alberto Coca |
URI: | http://hdl.handle.net/2445/213462 |
Appears in Collections: | Màster Oficial - Fonaments de la Ciència de Dades Programari - Treballs de l'alumnat |
Files in This Item:
File | Description | Size | Format | |
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codi_font.zip | Codi font | 4.65 MB | zip | View/Open |
tfm_honorato_lopez_iker.pdf | Memòria | 5.44 MB | Adobe PDF | View/Open |
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