AI-Supported Diagnostic of Depression Using Clinical Interviews: A Pilot Study

Bakir Hadžić, Julia Ohse, Michael Danner, Nicolina Peperkorn, Parvez Mohammed, Youssef Shiban, Matthias Rätsch

2024

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 an 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.

Paper Citation

in Harvard Style

Hadžić B., Ohse J., Danner M., Peperkorn N., Mohammed P., Shiban Y. and Rätsch M. (2024). AI-Supported Diagnostic of Depression Using Clinical Interviews: A Pilot Study. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: HUCAPP; ISBN 978-989-758-679-8, SciTePress, pages 500-507. DOI: 10.5220/0012439700003660

in Bibtex Style

@conference{hucapp24,
author={Bakir Hadžić and Julia Ohse and Michael Danner and Nicolina Peperkorn and Parvez Mohammed and Youssef Shiban and Matthias Rätsch},
title={AI-Supported Diagnostic of Depression Using Clinical Interviews: A Pilot Study},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: HUCAPP},
year={2024},
pages={500-507},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012439700003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: HUCAPP
TI - AI-Supported Diagnostic of Depression Using Clinical Interviews: A Pilot Study
SN - 978-989-758-679-8
AU - Hadžić B.
AU - Ohse J.
AU - Danner M.
AU - Peperkorn N.
AU - Mohammed P.
AU - Shiban Y.
AU - Rätsch M.
PY - 2024
SP - 500
EP - 507
DO - 10.5220/0012439700003660
PB - SciTePress