Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy

Evangelia Gogoulou, Magnus Boman, Fehmi Ben Abdesslem, Nils Hentati Isacsson, Viktor Kaldo, Magnus Sahlgren


Abstract
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.
Anthology ID:
2021.eacl-main.46
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
575–580
Language:
URL:
https://aclanthology.org/2021.eacl-main.46
DOI:
10.18653/v1/2021.eacl-main.46
Bibkey:
Cite (ACL):
Evangelia Gogoulou, Magnus Boman, Fehmi Ben Abdesslem, Nils Hentati Isacsson, Viktor Kaldo, and Magnus Sahlgren. 2021. Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 575–580, Online. Association for Computational Linguistics.
Cite (Informal):
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy (Gogoulou et al., EACL 2021)
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PDF:
https://aclanthology.org/2021.eacl-main.46.pdf