ArticleFrontiers in psychiatry2020
Machine Learning for Differential Diagnosis Between Clinical Conditions With Social Difficulty: Autism Spectrum Disorder, Early Psychosis, and Social Anxiety Disorder.
Article in Frontiers in psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- The relationships between anxiety, psychotic-like experiences and autism: a systematic review.Frontiers in psychology · 2025Pooled it
- Understanding Mechanisms that Maintain Social Anxiety Disorder in Autistic Individuals Through the Clark and Wells (1995) Model and Beyond: A Systematic Review.Clinical child and family psychology review · 2024Pooled it
- Review
- Prediction models for highly scalable technology-assisted differential diagnostics of autism spectrum disorder.BMJ mental health · 2026Article
- WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis.NPJ digital medicine · 2026Article
- How to Predict the Onset of Anxiety and Depression in Pediatric Residents? -- A Survey From China.Neuropsychiatric disease and treatment · 2026Article
- It is More Anxiousness than Role-playing: Social Camouflaging Conceptualization Among Adults on the Autism Spectrum Compared to Persons with Social Anxiety Disorder.Journal of autism and developmental disorders · 2025Article
- Development and Validation of Prediction Models for the Diagnosis of Autism Spectrum Disorder in a Korean General Population.JAACAP open · 2025Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Predictive modelling of deliberate self-harm and suicide attempts in young people accessing primary care: a machine learning analysis of a longitudinal study.Social psychiatry and psychiatric epidemiology · 2023Article
- A Machine Learning Approach to the Diagnosis of Autism Spectrum Disorder and Multi-Systemic Developmental Disorder Based on Retrospective Data and ADOS-2 Score.Brain sciences · 2023Article
- Recent advances in the understanding and psychological treatment of social anxiety disorder.Faculty reviews · 2023Review
- Predicting social anxiety in young adults with machine learning of resting-state brain functional radiomic features.Scientific reports · 2022Article
- Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.Journal of personalized medicine · 2021Review
- Predicting self-harm within six months after initial presentation to youth mental health services: A machine learning study.PloS one · 2020Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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