Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis

G. Bussu, E. J.H. Jones, T. Charman, M. H. Johnson, J. K. Buitelaar, The Basis Team, S. Baron-Cohen, R. Bedford, P. Bolton, A. Blasi, S. Chandler, C. Cheung, K. Davies, M. Elsabbagh, J. Fernandes, I. Gammer, H. Garwood, T. Gliga, J. Guiraud, K. HudryM. H. Johnson, M. Liew, S. Lloyd-Fox, H. Maris, L. O’Hara, G. Pasco, A. Pickles, H. Ribeiro, E. Salomone, L. Tucker, A. Volein

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)2418-2433
Number of pages16
JournalJournal of Autism and Developmental Disorders
Volume48
Early online date16 Feb 2018
DOIs
Publication statusPublished - 31 Jul 2018

Keywords

  • Autism
  • Data integration
  • Early prediction
  • High-risk
  • Individual prediction
  • Longitudinal study
  • Machine learning

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