Natural language processing for cognitive therapy: Extracting schemas from thought records.

The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The sch...

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Autores principales:	Franziska Burger, Mark A Neerincx, Willem-Paul Brinkman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a196d4287158496bb510192d30bff829
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spelling	oai:doaj.org-article:a196d4287158496bb510192d30bff8292021-12-02T20:16:50ZNatural language processing for cognitive therapy: Extracting schemas from thought records.1932-620310.1371/journal.pone.0257832https://doaj.org/article/a196d4287158496bb510192d30bff8292021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257832https://doaj.org/toc/1932-6203The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen's κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.Franziska BurgerMark A NeerincxWillem-Paul BrinkmanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0257832 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Franziska Burger Mark A Neerincx Willem-Paul Brinkman Natural language processing for cognitive therapy: Extracting schemas from thought records.
description	The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen's κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.
format	article
author	Franziska Burger Mark A Neerincx Willem-Paul Brinkman
author_facet	Franziska Burger Mark A Neerincx Willem-Paul Brinkman
author_sort	Franziska Burger
title	Natural language processing for cognitive therapy: Extracting schemas from thought records.
title_short	Natural language processing for cognitive therapy: Extracting schemas from thought records.
title_full	Natural language processing for cognitive therapy: Extracting schemas from thought records.
title_fullStr	Natural language processing for cognitive therapy: Extracting schemas from thought records.
title_full_unstemmed	Natural language processing for cognitive therapy: Extracting schemas from thought records.
title_sort	natural language processing for cognitive therapy: extracting schemas from thought records.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a196d4287158496bb510192d30bff829
work_keys_str_mv	AT franziskaburger naturallanguageprocessingforcognitivetherapyextractingschemasfromthoughtrecords AT markaneerincx naturallanguageprocessingforcognitivetherapyextractingschemasfromthoughtrecords AT willempaulbrinkman naturallanguageprocessingforcognitivetherapyextractingschemasfromthoughtrecords
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Natural language processing for cognitive therapy: Extracting schemas from thought records.

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