A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic instruments for autism

2022 | journal article. A publication with affiliation to the University of Göttingen.

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​A data driven machine learning approach to differentiate between autism spectrum disorder and attention-deficit/hyperactivity disorder based on the best-practice diagnostic instruments for autism​
Wolff, N.; Kohls, G.; Mack, J. T.; Vahid, A.; Elster, E. M.; Stroth, S. & Poustka, L. et al.​ (2022) 
Scientific Reports12(1).​ DOI: https://doi.org/10.1038/s41598-022-21719-x 

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Authors
Wolff, Nicole; Kohls, Gregor; Mack, Judith T.; Vahid, Amirali; Elster, Erik M.; Stroth, Sanna; Poustka, Luise; Kuepper, Charlotte; Roepke, Stefan; Kamp-Becker, Inge; Roessner, Veit
Abstract
Abstract Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are two frequently co-occurring neurodevelopmental conditions that share certain symptomatology, including social difficulties. This presents practitioners with challenging (differential) diagnostic considerations, particularly in clinically more complex cases with co-occurring ASD and ADHD. Therefore, the primary aim of the current study was to apply a data-driven machine learning approach (support vector machine) to determine whether and which items from the best-practice clinical instruments for diagnosing ASD (ADOS, ADI-R) would best differentiate between four groups of individuals referred to specialized ASD clinics (i.e., ASD, ADHD, ASD + ADHD, ND = no diagnosis). We found that a subset of five features from both ADOS (clinical observation) and ADI-R (parental interview) reliably differentiated between ASD groups (ASD & ASD + ADHD) and non-ASD groups (ADHD & ND), and these features corresponded to the social-communication but also restrictive and repetitive behavior domains. In conclusion, the results of the current study support the idea that detecting ASD in individuals with suspected signs of the diagnosis, including those with co-occurring ADHD, is possible with considerably fewer items relative to the original ADOS/2 and ADI-R algorithms (i.e., 92% item reduction) while preserving relatively high diagnostic accuracy. Clinical implications and study limitations are discussed.
Issue Date
2022
Journal
Scientific Reports 
eISSN
2045-2322
Language
English

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