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Predicting treatment outcome from patient texts: The case of internet-based cognitive behavioural therapy
RISE, Sweden.
KTH Royal instute of technology, Sweden.
RISE, Sweden.
Karolinska institutet, Sweden.
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2021 (English)In: EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Association for Computational Linguistics (ACL) , 2021, p. 575-580Conference paper, Published paper (Refereed)
Sustainable development
SDG 3: Ensure healthy lives and promote well-being for all at all ages
Abstract [en]

We investigate the feasibility of applying standard text categorisation methods to patient text in order to predict treatment outcome in Internet-based cognitive behavioural therapy. The data set is unique in its detail and size for regular care for depression, social anxiety, and panic disorder. Our results indicate that there is a signal in the depression data, albeit a weak one. We also perform terminological and sentiment analysis, which confirm those results. © 2021 Association for Computational Linguistics

Place, publisher, year, edition, pages
Association for Computational Linguistics (ACL) , 2021. p. 575-580
Keywords [en]
Computational linguistics, Sentiment analysis, Data set, Internet based, Social anxieties, Treatment outcomes, Patient treatment
National Category
Psychology
Research subject
Social Sciences, Psychology
Identifiers
URN: urn:nbn:se:lnu:diva-112655Scopus ID: 2-s2.0-85107290691ISBN: 9781954085022 (print)OAI: oai:DiVA.org:lnu-112655DiVA, id: diva2:1656826
Conference
16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, 19 April 2021 through 23 April 2021
Available from: 2022-05-08 Created: 2022-05-08 Last updated: 2022-05-09Bibliographically approved

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Kaldo, Viktor

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Citation style
  • apa
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Output format
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  • asciidoc
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