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Passive Diagnosis incorporating the PHQ-4 for Depression and Anxiety
Stockholm University, Faculty of Social Sciences, Department of Psychology, Clinical psychology. Linköping University, Sweden.ORCID iD: 0000-0001-5547-3866
2019 (English)In: Proceedings of the 4th Social Media Mining for Health Applications (#SMM4H): Workshop & Shared Task / [ed] Davy Weissenbacher, Graciela Gonzalez-Hernandez, Association for Computational Linguistics , 2019, p. 40-46Conference paper, Published paper (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Association for Computational Linguistics , 2019. p. 40-46
Keywords [en]
depression, anxiety, written text, passive diagnosis, PHQ-4
National Category
Psychology
Research subject
Psychology
Identifiers
URN: urn:nbn:se:su:diva-177845DOI: 10.18653/v1/W19-3205OAI: oai:DiVA.org:su-177845DiVA, id: diva2:1383788
Conference
The 4th Social Media Mining for Health Applications (#SMM4H), Workshop & Shared Task, Florence, Italy, August 2, 2019
Available from: 2020-01-08 Created: 2020-01-08 Last updated: 2020-03-05Bibliographically approved

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