Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety

Fionn Delahunty, Robert Johansson, Mihael Arcan


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
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)
Copy Citation:
PDF:
https://aclanthology.org/W19-3205.pdf