%0 Journal Article %1 2457 %A Shui, Amy M %A Katz, Terry F %A Malow, Beth A %A Mazurek, Micah O %D 2017 %K imported %T Predicting Sleep Problems Using Machine Learning in Children with Autism Spectrum Disorders %X Introduction -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. Methods -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. Conclusions -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.

@article{2457, abstract = {Introduction -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. Methods -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. Conclusions -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{\textquoteright}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. }, added-at = {2021-02-17T03:07:48.000+0100}, author = {Shui, Amy M and Katz, Terry F and Malow, Beth A and Mazurek, Micah O}, biburl = {https://www.bibsonomy.org/bibtex/215b2b5d916daa23b45afc02f9b8dbfac/acn}, interhash = {09b1df47cca5ef8965dc5e9d972d381b}, intrahash = {15b2b5d916daa23b45afc02f9b8dbfac}, keywords = {imported}, timestamp = {2021-02-17T03:08:36.000+0100}, title = {Predicting Sleep Problems Using Machine Learning in Children with Autism Spectrum Disorders}, year = 2017 }