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. 2023 Jan;64(1):16-26.
doi: 10.1111/jcpp.13650. Epub 2022 Jul 1.

Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses

Martin Schulte-Rüther  1   2 , Tomas Kulvicius  1   3 , Sanna Stroth  4 , Nicole Wolff  5 , Veit Roessner  5 , Peter B Marschik  1   2   6   7 , Inge Kamp-Becker  4 , Luise Poustka  1   2
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Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co-occurring diagnoses

Martin Schulte-Rüther et al. J Child Psychol Psychiatry. 2023 Jan.

Abstract

Background: Diagnostic assessment of ASD requires substantial clinical experience and is particularly difficult in the context of other disorders with behavioral symptoms in the domain of social interaction and communication. Observation measures such as the Autism Diagnostic Observation Schedule (ADOS) do not take into account such co-occurring disorders.

Method: We used a well-characterized clinical sample of individuals (n = 1,251) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n = 481) and covered a range of additional overlapping diagnoses, including anxiety-related disorders (ANX, n = 122), ADHD (n = 439), and conduct disorder (CD, n = 194). We focused on ADOS module 3, covering the age range with particular high prevalence of such differential diagnoses. We used machine learning (ML) and trained random forest models on ADOS single item scores to predict a clinical best-estimate diagnosis of ASD in the context of these differential diagnoses (ASD vs. ANX, ASD vs. ADHD, ASD vs. CD), in the context of co-occurring ADHD, and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance and made available a Webapp to showcase the results and feasibility for translation into clinical practice.

Results: We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89). In particular for individuals with less severe symptoms, our models showed increases of up to 35% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX, ADHD, and CD models in comparison with the unspecific model revealing distinct patterns of importance for specific ADOS items with respect to differential diagnoses.

Conclusions: ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from detailed diagnostic observation instruments such as the ADOS. Importantly, this strategy might be of particular relevance for older children with less severe symptoms for whom the diagnostic decision is often particularly difficult.

Keywords: Autism spectrum disorders; Machine learning; diagnosis.

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References

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