Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety

Fionn Delahunty, Robert Johansson, Mihael Arcan

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Abstract
Depression and anxiety are the two most prevalent mental health disorders worldwide, impacting the lives of millions of people each year. In this work, we develop and evaluate a multilabel, multidimensional deep neural network designed to predict PHQ-4 scores based on individuals written text. Our system outperforms random baseline metrics and provides a novel approach to how we can predict psychometric scores from written text. Additionally, we explore how this architecture can be applied to analyse social media data.
Anthology ID:
W19-3205
Volume:
Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
Month:
August
Year:
2019
Address:
Florence, Italy
Editors:
Davy Weissenbacher, Graciela Gonzalez-Hernandez
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
40–46
Language:
URL:
https://aclanthology.org/W19-3205
DOI:
10.18653/v1/W19-3205
Bibkey:
Cite (ACL):
Fionn Delahunty, Robert Johansson, and Mihael Arcan. 2019. Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety. In Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task, pages 40–46, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety (Delahunty et al., ACL 2019)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/W19-3205.pdf
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