Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders

Abstract Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participant...

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Autores principales:	Sunny X. Tang, Reno Kriz, Sunghye Cho, Suh Jung Park, Jenna Harowitz, Raquel E. Gur, Mahendra T. Bhati, Daniel H. Wolf, João Sedoc, Mark Y. Liberman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/5719c8246cbb4adb929bfe059cb11ecc
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spelling	oai:doaj.org-article:5719c8246cbb4adb929bfe059cb11ecc2021-12-02T15:43:16ZNatural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders10.1038/s41537-021-00154-32334-265Xhttps://doaj.org/article/5719c8246cbb4adb929bfe059cb11ecc2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41537-021-00154-3https://doaj.org/toc/2334-265XAbstract Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., “the,” “a,”). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.Sunny X. TangReno KrizSunghye ChoSuh Jung ParkJenna HarowitzRaquel E. GurMahendra T. BhatiDaniel H. WolfJoão SedocMark Y. LibermanNature PortfolioarticlePsychiatryRC435-571ENnpj Schizophrenia, Vol 7, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Psychiatry RC435-571
spellingShingle	Psychiatry RC435-571 Sunny X. Tang Reno Kriz Sunghye Cho Suh Jung Park Jenna Harowitz Raquel E. Gur Mahendra T. Bhati Daniel H. Wolf João Sedoc Mark Y. Liberman Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
description	Abstract Computerized natural language processing (NLP) allows for objective and sensitive detection of speech disturbance, a hallmark of schizophrenia spectrum disorders (SSD). We explored several methods for characterizing speech changes in SSD (n = 20) compared to healthy control (HC) participants (n = 11) and approached linguistic phenotyping on three levels: individual words, parts-of-speech (POS), and sentence-level coherence. NLP features were compared with a clinical gold standard, the Scale for the Assessment of Thought, Language and Communication (TLC). We utilized Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art embedding algorithm incorporating bidirectional context. Through the POS approach, we found that SSD used more pronouns but fewer adverbs, adjectives, and determiners (e.g., “the,” “a,”). Analysis of individual word usage was notable for more frequent use of first-person singular pronouns among individuals with SSD and first-person plural pronouns among HC. There was a striking increase in incomplete words among SSD. Sentence-level analysis using BERT reflected increased tangentiality among SSD with greater sentence embedding distances. The SSD sample had low speech disturbance on average and there was no difference in group means for TLC scores. However, NLP measures of language disturbance appear to be sensitive to these subclinical differences and showed greater ability to discriminate between HC and SSD than a model based on clinical ratings alone. These intriguing exploratory results from a small sample prompt further inquiry into NLP methods for characterizing language disturbance in SSD and suggest that NLP measures may yield clinically relevant and informative biomarkers.
format	article
author	Sunny X. Tang Reno Kriz Sunghye Cho Suh Jung Park Jenna Harowitz Raquel E. Gur Mahendra T. Bhati Daniel H. Wolf João Sedoc Mark Y. Liberman
author_facet	Sunny X. Tang Reno Kriz Sunghye Cho Suh Jung Park Jenna Harowitz Raquel E. Gur Mahendra T. Bhati Daniel H. Wolf João Sedoc Mark Y. Liberman
author_sort	Sunny X. Tang
title	Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
title_short	Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
title_full	Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
title_fullStr	Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
title_full_unstemmed	Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
title_sort	natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5719c8246cbb4adb929bfe059cb11ecc
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Natural language processing methods are sensitive to sub-clinical linguistic differences in schizophrenia spectrum disorders

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