Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty

Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder

Bibliographic Data

ID15520713
AuthorsEleni Demetriou (Center for Autism and Related Disorders), Eleni A Demetriou (0000-0001-7967-5972), Shin Park (0000-0002-1309-0506, Center for Autism and Related Disorders), Shin H Park, Nicholas Ho (0000-0001-9843-0866, The University of Sydney), Karen L Pepper (The University of Sydney), Yun Ju Christine Song (0000-0001-8776-6305, Center for Autism and Related Disorders), Sharon L Naismith (0000-0001-9076-2778, The University of Sydney), Emma E Thomas (0000-0002-0407-4686, The University of Sydney), Ian B Hickie (0000-0001-8832-9895, The University of Sydney), Adam J Guastella (0000-0001-8178-4625, The University of Sydney, corresponding author)
Year2020
Volume11
Pages545-545
Publication date2020-06-19
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2020.00545
PMID32636768
OpenAlexW3033396032
LanguageEN
References cited113

Differential diagnosis in adult cohorts with social difficulty is confounded by comorbid mental health conditions, common etiologies, and shared phenotypes. Identifying shared and discriminating profiles can facilitate intervention and remediation strategies. The objective of the study was to identify salient features of a composite test battery of cognitive and mood measures using a machine learning paradigm in clinical cohorts with social interaction difficulties. We recruited clinical participants who met standardized diagnostic criteria for autism spectrum disorder (ASD: n = 62), early psychosis (EP: n = 48), or social anxiety disorder (SAD: N = 83) and compared them with a neurotypical comparison group (TYP: N = 43). Using five machine-learning algorithms and repeated cross-validation, we trained and tested classification models using measures of cognitive and executive function, lower- and higher-order social cognition and mood severity. Performance metrics were the area under the curve (AUC) and Brier Scores. Sixteen features successfully differentiated between the groups. The control versus social impairment cohorts (ASD, EP, SAD) were differentiated by social cognition, visuospatial memory and mood measures. Importantly, a distinct profile cluster drawn from social cognition, visual learning, executive function and mood, distinguished the neurodevelopmental cohort (EP and ASD) from the SAD group. The mean AUC range was between 0.891 and 0.916 for social impairment versus control cohorts and, 0.729 to 0.781 for SAD vs neurodevelopmental cohorts. This is the first study that compares an extensive battery of neuropsychological and self-report measures using a machine learning protocol in clinical and neurodevelopmental cohorts characterized by social impairment. Findings are relevant for diagnostic, intervention and remediation strategies for these groups

Anxiety · Autism · Autism spectrum disorder · Cognition · Cohort · Mood · Neurodevelopmental disorder · Neurotypical · Psychiatry · Social anxiety · Social cognition · Attention Deficit Hyperactivity Disorder · Autism Spectrum Disorder Research · Clinical Psychology · Medicine · Obsessive-Compulsive Spectrum Disorders · Psychology

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