ABSTRACT
Sleep apnea in children is a major health problem affecting one to five percent of children (in the US). If not treated in a timely manner, it can also lead to other physical and mental health issues. Pediatric sleep apnea has different clinical causes and characteristics than adults. Despite a large group of studies dedicated to studying adult apnea, pediatric sleep apnea has been studied in a much less limited fashion. Relatedly, at-home sleep apnea testing tools and algorithmic methods for automatic detection of sleep apnea are widely present for adults, but not children. In this study, we target this gap by presenting a machine learning-based model for detecting apnea events from commonly collected sleep signals. We show that our method outperforms state-of-the-art methods across two public datasets, as determined by the F1-score and AUROC measures. Additionally, we show that using two of the signals that are easier to collect at home (ECG and SpO2) can also achieve very competitive results, potentially addressing the concerns about collecting various sleep signals from children outside the clinic. Therefore, our study can greatly inform ongoing progress toward increasing the accessibility of pediatric sleep apnea testing and improving the timeliness of the treatment interventions.
ABSTRACT
Obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity who are not able to be successfully managed in the primary care setting. Despite their great potential, high dropout rates (referred to as attrition) are a major hurdle in delivering successful interventions. Predicting attrition patterns can help providers reduce the alarmingly high rates of attrition (up to 80%) by engaging in earlier and more personalized interventions. Previous work has mainly focused on finding static predictors of attrition on smaller datasets and has achieved limited success in effective prediction. In this study, we have collected a five-year comprehensive dataset of 4,550 children from diverse backgrounds receiving treatment at four pediatric weight management programs in the US. We then developed a machine learning pipeline to predict (a) the likelihood of attrition, and (b) the change in body-mass index (BMI) percentile of children, at different time points after joining the weight management program. Our pipeline is greatly customized for this problem using advanced machine learning techniques to process longitudinal data, smaller-size data, and interrelated prediction tasks. The proposed method showed strong prediction performance as measured by AUROC scores (average AUROC of 0.77 for predicting attrition, and 0.78 for predicting weight outcomes).
