Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder - Demetriou, Eleni A.; Park, Shin H.; Ho, Nicholas; Pepper, Karen L.; Song, Yun J. C.; Naismith, Sharon L.; Thomas, Emma E.; Hickie, Ian B.; Guastella, Adam J. (Frontiers in Psychiatry, 2020)
Differential diagnosis in adult cohorts with social difficulty is confounded by comorbid mental health conditions, common aetiologies 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 standardised 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 0.892 and 0.915 for social impairment versus control cohorts and, 0.731 to 0.777 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 characterised by social impairment. Findings are relevant for diagnostic, intervention and remediation strategies for these groups.