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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7080741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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