Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder

被引：13

作者：

Demetriou, Eleni A. ^{[1
]}

Park, Shin H. ^{[1
]}

Ho, Nicholas ^{[1
]}

Pepper, Karen L. ^{[1
]}

Song, Yun J. C. ^{[1
]}

Naismith, Sharon L. ^{[2
]}

Thomas, Emma E. ^{[1
]}

Hickie, Ian B. ^{[1
,3
]}

Guastella, Adam J. ^{[1
,3
]}

机构：

[1] Univ Sydney, Childrens Hosp Westmead Clin Sch 1, Child Neurodev & Mental Hlth Team, Brain & Mind Ctr,Fac Med & Hlth,Autism Clin Trans, Sydney, NSW, Australia

[2] Univ Sydney, Sch Psychol, Sydney, NSW, Australia

[3] Univ Sydney, Fac Med & Hlth, Youth Mental Hlth Unit, Cent Clin Sch,Brain & Mind Ctr, Sydney, NSW, Australia

来源：

FRONTIERS IN PSYCHIATRY | 2020年 / 11卷

基金：

澳大利亚国家健康与医学研究理事会; 英国医学研究理事会;

关键词：

autism spectrum disorder; cognition; differential diagnosis; early psychosis; machine learning; social anxiety disorder; HIGH-FUNCTIONING AUTISM; EXECUTIVE FUNCTION; 1ST-EPISODE PSYCHOSIS; ASPERGER-SYNDROME; NORMATIVE DATA; TRAIT ANXIETY; VISUAL MEMORY; SCHIZOPHRENIA; ADULTS; ASSOCIATION;

D O I：

10.3389/fpsyt.2020.00545

中图分类号：

R749 [精神病学];

学科分类号：

100205 ;

摘要：

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.

引用

页数：16