English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Childhood obesity is a major public health concern. Multidisciplinarypediatric weight management programs are considered standard treatment forchildren with obesity and severe obesity who are not able to be successfullymanaged in the primary care setting; however, high drop-out rates (referred toas attrition) are a major hurdle in delivering successful interventions.Predicting attrition patterns can help providers reduce the attrition rates.Previous work has mainly focused on finding static predictors of attritionusing statistical analysis methods. In this study, we present a machinelearning model to predict (a) the likelihood of attrition, and (b) the changein body-mass index (BMI) percentile of children, at different time points afterjoining a weight management program. We use a five-year dataset containing theinformation related to around 4,550 children that we have compiled using datafrom the Nemours Pediatric Weight Management program. Our models show strongprediction performance as determined by high AUROC scores across differenttasks (average AUROC of 0.75 for predicting attrition, and 0.73 for predictingweight outcomes). Additionally, we report the top features predicting attritionand weight outcomes in a series of explanatory experiments.

Who will Leave a Pediatric Weight Management Program and When? -- A machine learning approach for predicting attrition patterns