Bussu, G. and Jones, Emily J.H. and Charman, T. and Johnson, Mark H. and Buitelaar, J.K. (2018) Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis. Journal of Autism and Developmental Disorders , ISSN 0162-3257.
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Abstract
We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and HR-Typical showed higher developmental level and functioning, and fewer ASD symptoms than HR-Atypical and HR-ASD. At 8 months, machine learning classified HR-ASD at chance level, and broader atypical development with 69.2% Area Under the Curve (AUC). At 14 months, ASD and broader atypical development were classified with approximately 71% AUC. Thus, prediction of ASD was only possible with moderate accuracy at 14 months.
Metadata
| Item Type: | Article |
|---|---|
| Keyword(s) / Subject(s): | Autism, Data integration, Early prediction, High-risk, Individual prediction, Longitudinal study, Machine learning |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Psychological Sciences |
| SWORD Depositor: | Mr Joe Tenant |
| Depositing User: | Mr Joe Tenant |
| Date Deposited: | 14 Mar 2018 12:33 |
| Last Modified: | 02 Aug 2023 17:39 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/21440 |
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