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Predicting treatment outcome from patient texts: The case of internet-based cognitive behavioural therapy
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.ORCID iD: 0000-0001-7949-1815
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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)
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.

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
Applied Psychology
Identifiers
URN: urn:nbn:se:kth:diva-309678ISI: 000863557000046Scopus ID: 2-s2.0-85107290691OAI: oai:DiVA.org:kth-309678DiVA, id: diva2:1645054
Conference
16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, 19 April 2021 through 23 April 2021
Note

Part of proceedings: ISBN 9781954085022, QC 20230117

Available from: 2022-03-16 Created: 2022-03-16 Last updated: 2024-09-23Bibliographically approved

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Gogoulou, EvangeliaBoman, Magnus

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf