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Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning

Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques su...

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Autores principales: Küpper, Charlotte, Stroth, Sanna, Wolff, Nicole, Hauck, Florian, Kliewer, Natalia, Schad-Hansjosten, Tanja, Kamp-Becker, Inge, Poustka, Luise, Roessner, Veit, Schultebraucks, Katharina, Roepke, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080741/
https://www.ncbi.nlm.nih.gov/pubmed/32188882
http://dx.doi.org/10.1038/s41598-020-61607-w
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author Küpper, Charlotte
Stroth, Sanna
Wolff, Nicole
Hauck, Florian
Kliewer, Natalia
Schad-Hansjosten, Tanja
Kamp-Becker, Inge
Poustka, Luise
Roessner, Veit
Schultebraucks, Katharina
Roepke, Stefan
author_facet Küpper, Charlotte
Stroth, Sanna
Wolff, Nicole
Hauck, Florian
Kliewer, Natalia
Schad-Hansjosten, Tanja
Kamp-Becker, Inge
Poustka, Luise
Roessner, Veit
Schultebraucks, Katharina
Roepke, Stefan
author_sort Küpper, Charlotte
collection PubMed
description Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis.
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spelling pubmed-70807412020-03-23 Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning Küpper, Charlotte Stroth, Sanna Wolff, Nicole Hauck, Florian Kliewer, Natalia Schad-Hansjosten, Tanja Kamp-Becker, Inge Poustka, Luise Roessner, Veit Schultebraucks, Katharina Roepke, Stefan Sci Rep Article Diagnosing autism spectrum disorders (ASD) is a complicated, time-consuming process which is particularly challenging in older individuals. One of the most widely used behavioral diagnostic tools is the Autism Diagnostic Observation Schedule (ADOS). Previous work using machine learning techniques suggested that ASD detection in children can be achieved with substantially fewer items than the original ADOS. Here, we expand on this work with a specific focus on adolescents and adults as assessed with the ADOS Module 4. We used a machine learning algorithm (support vector machine) to examine whether ASD detection can be improved by identifying a subset of behavioral features from the ADOS Module 4 in a routine clinical sample of N = 673 high-functioning adolescents and adults with ASD (n = 385) and individuals with suspected ASD but other best-estimate or no psychiatric diagnoses (n = 288). We identified reduced subsets of 5 behavioral features for the whole sample as well as age subgroups (adolescents vs. adults) that showed good specificity and sensitivity and reached performance close to that of the existing ADOS algorithm and the full ADOS, with no significant differences in overall performance. These results may help to improve the complicated diagnostic process of ASD by encouraging future efforts to develop novel diagnostic instruments for ASD detection based on the identified constructs as well as aiding clinicians in the difficult question of differential diagnosis. Nature Publishing Group UK 2020-03-18 /pmc/articles/PMC7080741/ /pubmed/32188882 http://dx.doi.org/10.1038/s41598-020-61607-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Küpper, Charlotte
Stroth, Sanna
Wolff, Nicole
Hauck, Florian
Kliewer, Natalia
Schad-Hansjosten, Tanja
Kamp-Becker, Inge
Poustka, Luise
Roessner, Veit
Schultebraucks, Katharina
Roepke, Stefan
Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
title Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
title_full Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
title_fullStr Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
title_full_unstemmed Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
title_short Identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
title_sort identifying predictive features of autism spectrum disorders in a clinical sample of adolescents and adults using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080741/
https://www.ncbi.nlm.nih.gov/pubmed/32188882
http://dx.doi.org/10.1038/s41598-020-61607-w
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