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        <title>Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety</title>
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        <namePart type="given">Fionn</namePart>
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        <namePart type="given">Robert</namePart>
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        <namePart type="given">Mihael</namePart>
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            <title>Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop &amp; Shared Task</title>
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        <name type="personal">
            <namePart type="given">Davy</namePart>
            <namePart type="family">Weissenbacher</namePart>
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        <name type="personal">
            <namePart type="given">Graciela</namePart>
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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.</abstract>
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    <identifier type="doi">10.18653/v1/W19-3205</identifier>
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%0 Conference Proceedings
%T Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety
%A Delahunty, Fionn
%A Johansson, Robert
%A Arcan, Mihael
%Y Weissenbacher, Davy
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F delahunty-etal-2019-passive
%X 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.
%R 10.18653/v1/W19-3205
%U https://aclanthology.org/W19-3205
%U https://doi.org/10.18653/v1/W19-3205
%P 40-46

Markdown (Informal)

[Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety](https://aclanthology.org/W19-3205) (Delahunty et al., ACL 2019)

• Passive Diagnosis Incorporating the PHQ-4 for Depression and Anxiety (Delahunty et al., ACL 2019)

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.