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Predicting Sleep Problems Using Machine Learning in Children with Autism Spectrum Disorders

TitlePredicting Sleep Problems Using Machine Learning in Children with Autism Spectrum Disorders
Publication TypeConference Abstract
Year of Publication2017
AuthorsShui, AM, Katz, TF, Malow, BA, Mazurek, MO
Conference NameIMFAR

-Sleep difficulties are a common problem in children with autism spectrum disorder (ASD) Of the children in the Autism Speaks-Autism Treatment Network (ATN) registry who do not have any parent-reported sleep problems at baseline (58%), a substantial subset have sleep problems reported at first follow-up (20.5%). -Developing a predictive model for parent-reported sleep problems using longitudinal data and machine learning could help with treatment and prevention of these problems.

-A sample of children in the ATN registry without parent-reported sleep problems at baseline and with complete sleep data at first-follow-up was randomly split into training (n=527) and test samples (n=518). -88 training sample baseline characteristics recommended by a clinician were tested for associations with subsequent sleep problems. -Model predictors were chosen based on statistical significance and clinical importance, correlation and multicollinearity considerations, and comparison of c-statistics from alternative logistic regression models. -Given probabilities of sleep problems from the final model, a threshold for classifying children as at risk was selected that yielded at least 85% sensitivity and maintained maximum associated specificity. -Each child in the test sample was scored and assigned a predicted sleep problem status based on the model threshold, and comparison of predicted and true status yielded sensitivity, specificity, PPV, NPV, and overall accuracy.

-Among children with ASD, those with ENT problems, asthma, more anxious/depressed and aggressive behavior, and less educated parents at baseline may present with more sleep problems during a follow-up visit. -In a multivariable model, aggressive behavior independently predicts sleep problems. -The model’s high sensitivity for identifying children at risk and its accurate prediction of low risk can help with treatment and prevention of sleep problems. -Further data collection may provide better prediction through methods requiring larger samples.