<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chen-etal-2023-detection">
    <titleInfo>
        <title>Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Siyuan</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhiling</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mengyue</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kenny</namePart>
        <namePart type="family">Zhu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Existing Mental Disease Detection (MDD) research largely studies the detection of a single disorder, overlooking the fact that mental diseases might occur in tandem. Many approaches are not backed by domain knowledge (e.g., psychiatric symptoms) and thus fail to produce interpretable results. To tackle these issues, we propose an MDD framework that is capable of learning the shared clues of all diseases, while also capturing the specificity of each single disease. The two-stream architecture which simultaneously processes text and symptom features can combine the strength of both modalities and offer knowledge-based explainability. Experiments on the detection of 7 diseases show that our model can boost detection performance by more than 10%, especially in relatively rare classes.</abstract>
    <identifier type="citekey">chen-etal-2023-detection</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.562</identifier>
    <location>
        <url>https://aclanthology.org/2023.emnlp-main.562</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>9071</start>
            <end>9084</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts
%A Chen, Siyuan
%A Zhang, Zhiling
%A Wu, Mengyue
%A Zhu, Kenny
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F chen-etal-2023-detection
%X Existing Mental Disease Detection (MDD) research largely studies the detection of a single disorder, overlooking the fact that mental diseases might occur in tandem. Many approaches are not backed by domain knowledge (e.g., psychiatric symptoms) and thus fail to produce interpretable results. To tackle these issues, we propose an MDD framework that is capable of learning the shared clues of all diseases, while also capturing the specificity of each single disease. The two-stream architecture which simultaneously processes text and symptom features can combine the strength of both modalities and offer knowledge-based explainability. Experiments on the detection of 7 diseases show that our model can boost detection performance by more than 10%, especially in relatively rare classes.
%R 10.18653/v1/2023.emnlp-main.562
%U https://aclanthology.org/2023.emnlp-main.562
%U https://doi.org/10.18653/v1/2023.emnlp-main.562
%P 9071-9084

Markdown (Informal)

[Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts](https://aclanthology.org/2023.emnlp-main.562) (Chen et al., EMNLP 2023)

• Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (Chen et al., EMNLP 2023)

ACL

• Siyuan Chen, Zhiling Zhang, Mengyue Wu, and Kenny Zhu. 2023. Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9071–9084, Singapore. Association for Computational Linguistics.