Authors:
Bakir Hadžić
1
;
Julia Ohse
2
;
Michael Danner
3
;
Nicolina Peperkorn
2
;
Parvez Mohammed
1
;
Youssef Shiban
2
and
Matthias Rätsch
1
Affiliations:
1
ViSiR, Reutlingen University, Reutlingen, Germany
;
2
Private University of Applied Sciences, Göttingen, Germany
;
3
CVSSP, University of Surrey, Guildford, U.K.
Keyword(s):
Deep Learning, Depression Diagnostics, Mental Health, NLP, BERT, GPT.
Abstract:
In the face of rising depression rates, the urgency of early and accurate diagnosis has never been more
paramount. Traditional diagnostic methods, while invaluable, can sometimes be limited in access and susceptible to biases, potentially leading to underdiagnoses. This paper explores the innovative potential of AI
technology, specifically machine learning, as a diagnostic tool for depression. Drawing from prior research,
we note the success of machine learning in discerning depression indicators on social media platforms and
through automated interviews. A particular focus is given to the BERT-based NLP transformer model, previously shown to be effective in detecting depression from simulated interview data. Our study assessed this
model’s capability to identify depression from transcribed, semi-structured clinical interviews within a general
population sample. While the BERT model displayed an accuracy of 0.71, it was surpassed by an untrained
GPT-3.5 model, which achieved a
n impressive accuracy of 0.88. These findings emphasise the transformative
potential of NLP transformer models in the realm of depression detection. However, given the relatively small
dataset (N = 17) utilised, we advise a measured interpretation of the results. This paper is designed as a pilot
study, and further studies will incorporate bigger datasets.
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