Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.

During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., 'displays warmth and confidence', or 'attempts to set up collaboration') to facilitate the evaluation of the session. Those constructs, traditionally scor...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nikolaos Flemotomos, Victor R Martinez, Zhuohao Chen, Torrey A Creed, David C Atkins, Shrikanth Narayanan
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a11d41c49d464f78977237c20a54babe
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a11d41c49d464f78977237c20a54babe
record_format	dspace
spelling	oai:doaj.org-article:a11d41c49d464f78977237c20a54babe2021-12-02T20:16:36ZAutomated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.1932-620310.1371/journal.pone.0258639https://doaj.org/article/a11d41c49d464f78977237c20a54babe2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258639https://doaj.org/toc/1932-6203During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., 'displays warmth and confidence', or 'attempts to set up collaboration') to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS.Nikolaos FlemotomosVictor R MartinezZhuohao ChenTorrey A CreedDavid C AtkinsShrikanth NarayananPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258639 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nikolaos Flemotomos Victor R Martinez Zhuohao Chen Torrey A Creed David C Atkins Shrikanth Narayanan Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
description	During a psychotherapy session, the counselor typically adopts techniques which are codified along specific dimensions (e.g., 'displays warmth and confidence', or 'attempts to set up collaboration') to facilitate the evaluation of the session. Those constructs, traditionally scored by trained human raters, reflect the complex nature of psychotherapy and highly depend on the context of the interaction. Recent advances in deep contextualized language models offer an avenue for accurate in-domain linguistic representations which can lead to robust recognition and scoring of such psychotherapy-relevant behavioral constructs, and support quality assurance and supervision. In this work, we propose a BERT-based model for automatic behavioral scoring of a specific type of psychotherapy, called Cognitive Behavioral Therapy (CBT), where prior work is limited to frequency-based language features and/or short text excerpts which do not capture the unique elements involved in a spontaneous long conversational interaction. The model focuses on the classification of therapy sessions with respect to the overall score achieved on the widely-used Cognitive Therapy Rating Scale (CTRS), but is trained in a multi-task manner in order to achieve higher interpretability. BERT-based representations are further augmented with available therapy metadata, providing relevant non-linguistic context and leading to consistent performance improvements. We train and evaluate our models on a set of 1,118 real-world therapy sessions, recorded and automatically transcribed. Our best model achieves an F1 score equal to 72.61% on the binary classification task of low vs. high total CTRS.
format	article
author	Nikolaos Flemotomos Victor R Martinez Zhuohao Chen Torrey A Creed David C Atkins Shrikanth Narayanan
author_facet	Nikolaos Flemotomos Victor R Martinez Zhuohao Chen Torrey A Creed David C Atkins Shrikanth Narayanan
author_sort	Nikolaos Flemotomos
title	Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
title_short	Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
title_full	Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
title_fullStr	Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
title_full_unstemmed	Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
title_sort	automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a11d41c49d464f78977237c20a54babe
work_keys_str_mv	AT nikolaosflemotomos automatedqualityassessmentofcognitivebehavioraltherapysessionsthroughhighlycontextualizedlanguagerepresentations AT victorrmartinez automatedqualityassessmentofcognitivebehavioraltherapysessionsthroughhighlycontextualizedlanguagerepresentations AT zhuohaochen automatedqualityassessmentofcognitivebehavioraltherapysessionsthroughhighlycontextualizedlanguagerepresentations AT torreyacreed automatedqualityassessmentofcognitivebehavioraltherapysessionsthroughhighlycontextualizedlanguagerepresentations AT davidcatkins automatedqualityassessmentofcognitivebehavioraltherapysessionsthroughhighlycontextualizedlanguagerepresentations AT shrikanthnarayanan automatedqualityassessmentofcognitivebehavioraltherapysessionsthroughhighlycontextualizedlanguagerepresentations
_version_	1718374484991279104

Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations.

Ejemplares similares