Background: Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls . In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. Methods: The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results: The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions: ML is useful for creating robust, customizable instrument algorithms. In a unique dataset comprised of controls with other difficulties, our findings highlight the limitations of current caregiver-report instruments and indicate possible avenues for improving ASD screening and diagnostic tools.
Using data from the Autism Diagnostic Interview-Revised and The Social Responsiveness Scale, Bone et al. REF applied a machine learning classifier to a large group of verbal individuals with ASC and those with non-ASC disorders.
Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi‐instrument fusion
Journal of child psychology and psychiatry, and allied disciplines
Journal of child psychology and psychiatry, and allied disciplines
Psychology Medicine