Evaluating atypical language in autism using automated language measures

Abstract Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) age...

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Autores principales:	Alexandra C. Salem, Heather MacFarlane, Joel R. Adams, Grace O. Lawley, Jill K. Dolata, Steven Bedrick, Eric Fombonne
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1f267e951b0e4510933249a4537063a3
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spelling	oai:doaj.org-article:1f267e951b0e4510933249a4537063a32021-12-02T15:00:55ZEvaluating atypical language in autism using automated language measures10.1038/s41598-021-90304-52045-2322https://doaj.org/article/1f267e951b0e4510933249a4537063a32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90304-5https://doaj.org/toc/2045-2322Abstract Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p $$= .07$$ = . 07 ), nonparametric ANOVAs showed significant group differences (p $$< 0.01$$ < 0.01 ). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p $$< 0.01$$ < 0.01 ) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9–75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.Alexandra C. SalemHeather MacFarlaneJoel R. AdamsGrace O. LawleyJill K. DolataSteven BedrickEric FombonneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Alexandra C. Salem Heather MacFarlane Joel R. Adams Grace O. Lawley Jill K. Dolata Steven Bedrick Eric Fombonne Evaluating atypical language in autism using automated language measures
description	Abstract Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 participants (96 ASD, 28 TD, 45 ADHD) ages 7 to 17 were evaluated with the Autism Diagnostic Observation Schedule. Transcripts of one task were analyzed to generate seven ALMs: mean length of utterance in morphemes, number of different word roots (NDWR), um proportion, content maze proportion, unintelligible proportion, c-units per minute, and repetition proportion. With the exception of repetition proportion (p $$= .07$$ = . 07 ), nonparametric ANOVAs showed significant group differences (p $$< 0.01$$ < 0.01 ). The TD and ADHD groups did not differ from each other in post-hoc analyses. With the exception of NDWR, the ASD group showed significantly (p $$< 0.01$$ < 0.01 ) lower scores than both comparison groups. The ALMs were correlated with standardized clinical and language evaluations of ASD. In age- and IQ-adjusted logistic regression analyses, four ALMs significantly predicted ASD status with satisfactory accuracy (67.9–75.5%). When ALMs were combined together, accuracy improved to 82.4%. These ALMs offer a promising approach for generating novel outcome measures.
format	article
author	Alexandra C. Salem Heather MacFarlane Joel R. Adams Grace O. Lawley Jill K. Dolata Steven Bedrick Eric Fombonne
author_facet	Alexandra C. Salem Heather MacFarlane Joel R. Adams Grace O. Lawley Jill K. Dolata Steven Bedrick Eric Fombonne
author_sort	Alexandra C. Salem
title	Evaluating atypical language in autism using automated language measures
title_short	Evaluating atypical language in autism using automated language measures
title_full	Evaluating atypical language in autism using automated language measures
title_fullStr	Evaluating atypical language in autism using automated language measures
title_full_unstemmed	Evaluating atypical language in autism using automated language measures
title_sort	evaluating atypical language in autism using automated language measures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1f267e951b0e4510933249a4537063a3
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Evaluating atypical language in autism using automated language measures

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