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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W19-3205.pdf