AI-enhanced Mental Health Diagnosis: Leveraging Transformers for Early Detection of Depression Tendency in Textual Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149784" target="_blank" >RIV/00216305:26220/23:PU149784 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10333301" target="_blank" >https://ieeexplore.ieee.org/document/10333301</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICUMT61075.2023.10333301" target="_blank" >10.1109/ICUMT61075.2023.10333301</a>

Jazyk výsledku

angličtina

Název v původním jazyce

AI-enhanced Mental Health Diagnosis: Leveraging Transformers for Early Detection of Depression Tendency in Textual Data

Popis výsledku v původním jazyce

Early detection and treatment of depression depend critically on mental health assessment. Artificial intelligence-based methods have shown potential in assessing linguistic and cognitive patterns to spot those who are at risk of depression. This study tries to identify depression tendencies based on linguistic and cognitive characteristics by utilizing a transformer-based language model and self-attention. The goal is to assess how well the trained model performs at correctly identifying people who are predisposed to depression. A variant of the BERT (Bidirectional Encoder Representations from Transformers) model, trained on a larger corpus and for a longer duration, results in improved performance due to its strong capabilities in understanding natural language in the context of detecting depression using text data. The model can classify new text inputs and identify potential signs of depression by learning various linguistic cues and characteristics associated with depression such as sentiment patterns, language usage, emotional expressions, and topics discussed. The study also evaluates the efficacy of other machine learning classification models and long short-term memory networks in detecting depression tendencies. The proposed model achieves an astounding accuracy of 96.86% and is proven as the most effective model for detecting depressive tendencies. It underlines the usefulness of the proposed model in early detection and intervention programs for depression. This study advances mental health assessment and offers important insights into the recognition of mental health concerns by utilizing deep learning and natural language processing models.

Název v anglickém jazyce

AI-enhanced Mental Health Diagnosis: Leveraging Transformers for Early Detection of Depression Tendency in Textual Data

Popis výsledku anglicky

Early detection and treatment of depression depend critically on mental health assessment. Artificial intelligence-based methods have shown potential in assessing linguistic and cognitive patterns to spot those who are at risk of depression. This study tries to identify depression tendencies based on linguistic and cognitive characteristics by utilizing a transformer-based language model and self-attention. The goal is to assess how well the trained model performs at correctly identifying people who are predisposed to depression. A variant of the BERT (Bidirectional Encoder Representations from Transformers) model, trained on a larger corpus and for a longer duration, results in improved performance due to its strong capabilities in understanding natural language in the context of detecting depression using text data. The model can classify new text inputs and identify potential signs of depression by learning various linguistic cues and characteristics associated with depression such as sentiment patterns, language usage, emotional expressions, and topics discussed. The study also evaluates the efficacy of other machine learning classification models and long short-term memory networks in detecting depression tendencies. The proposed model achieves an astounding accuracy of 96.86% and is proven as the most effective model for detecting depressive tendencies. It underlines the usefulness of the proposed model in early detection and intervention programs for depression. This study advances mental health assessment and offers important insights into the recognition of mental health concerns by utilizing deep learning and natural language processing models.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/FW03010273" target="_blank" >FW03010273: Defektoskopie lakovaných dílů s pomocí automatické adaptace neuronových sítí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 15th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

ISBN

979-8-3503-9328-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

56-61

Název nakladatele

IEEE Computer Society

Místo vydání

neuveden

Místo konání akce

Gent, Belgium

Datum konání akce

30. 10. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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