Using machine learning to improve diagnostic assessment of ASD in the light of specific differential diagnosis

Abstract

Background Diagnostic assessment of ASD requires substantial clinical experience and is particular 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 comorbid and differential disorders. Method We used a well-characterized clinical sample of individuals (n=1262) that had received detailed outpatient evaluation for the presence of an ASD diagnosis (n=481) and covered a range of additional differential or overlapping diagnoses, including anxiety related disorders (ANX, n=100), ADHD (n=440), and conduct disorder (CD, n=192). 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) and an unspecific model using all available data. We employed nested cross-validation for an unbiased estimate of classification performance (ASD vs. non-ASD). Results We obtained very good overall sensitivity (0.89-0.94) and specificity (0.87-0.89) for the classification of ASD vs. non-ASD. In particular for individuals with less severe symptoms (around the ADOS cut-off) our models showed increases of up to 20% in sensitivity or specificity. Furthermore, we analyzed item importance profiles of the ANX-, ADHD- and CD-models in comparison to the unspecific model. These analyses revealed distinct patterns of importance for specific ADOS-items with respect to differential diagnoses. Conclusion Using ML-based diagnostic classification may improve clinical decisions by utilizing the full range of information from comprehensive and detailed diagnostic observation such as the ADOS. Importantly, this strategy might be of particular relevance for individuals with less severe symptoms that typically present a very difficult decision for the clinician.