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

• URL: http://arxiv.org/abs/2202.01765v1
• Date: Thu, 3 Feb 2022 18:41:36 GMT
• Title: Who will Leave a Pediatric Weight Management Program and When? -- A machine learning approach for predicting attrition patterns
• Authors: Hamed Fayyaz, Thao-Ly T. Phan, H. Timothy Bunnell, Rahmatollah Beheshti
• Abstract summary: Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity and severe obesity. High drop-out rates (referred to as attrition) are a major hurdle in delivering successful interventions. We present a machine learning model 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 a weight management program.
• Score: 1.0705399532413615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Childhood obesity is a major public health concern. Multidisciplinary pediatric weight management programs are considered standard treatment for children with obesity and severe obesity who are not able to be successfully managed in the primary care setting; however, high drop-out rates (referred to as 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 attrition using statistical analysis methods. In this study, we present a machine learning model 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 a weight management program. We use a five-year dataset containing the information related to around 4,550 children that we have compiled using data from the Nemours Pediatric Weight Management program. Our models show strong prediction performance as determined by high AUROC scores across different tasks (average AUROC of 0.75 for predicting attrition, and 0.73 for predicting weight outcomes). Additionally, we report the top features predicting attrition and weight outcomes in a series of explanatory experiments.

Related papers

• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data [1.3597551064547502]
  We have analyzed the classification performance of various machine learning models for fetal health analysis. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%.
  arXiv  Detail & Related papers  (2023-11-18T04:01:46Z)
• Development and validation of an interpretable machine learning-based calculator for predicting 5-year weight trajectories after bariatric surgery: a multinational retrospective cohort SOPHIA study [0.19676506937647395]
  We developed a model using machine learning to provide individual preoperative prediction of 5-year weight loss trajectories after surgery. Model is incorporated in an easy to use and interpretable web-based prediction tool to help inform clinical decision before surgery.
  arXiv  Detail & Related papers  (2023-08-31T09:30:06Z)
• DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework [27.82565790353953]
  Childhood and adolescent obesity rates are a global concern because obesity is associated with chronic diseases and long-term health risks. This study emphasizes the importance of early identification and prevention of obesity-related health issues. Factors such as height, weight, waist circumference, calorie intake, physical activity levels, and other relevant health information need to be considered for developing robust algorithms for obesity rate prediction.
  arXiv  Detail & Related papers  (2023-08-28T15:40:31Z)
• Body Fat Estimation from Surface Meshes using Graph Neural Networks [48.85291874087541]
  We show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area.
  arXiv  Detail & Related papers  (2023-07-13T10:21:34Z)
• Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning [51.487856868285995]
  We first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches.
  arXiv  Detail & Related papers  (2023-03-28T00:44:06Z)
• Human Health Indicator Prediction from Gait Video [34.24448186464565]
  We propose to employ gait videos to predict health indicators, which are more prevalent in surveillance and home monitoring scenarios. To better suit the health indicator prediction task, we bring forward Global-Local Aware aNdsymmetric Centro (GLANCE) module. Experiments demonstrate that the proposed paradigm achieves state-of-the-art results for predicting health indicators on MoVi.
  arXiv  Detail & Related papers  (2022-12-25T19:10:37Z)
• A Machine Learning Model for Predicting, Diagnosing, and Mitigating Health Disparities in Hospital Readmission [0.0]
  We propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions. We evaluate the performance of the proposed method on a clinical dataset using accuracy and fairness measures.
  arXiv  Detail & Related papers  (2022-06-13T16:07:25Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• UNITE: Uncertainty-based Health Risk Prediction Leveraging Multi-sourced Data [81.00385374948125]
  We present UNcertaInTy-based hEalth risk prediction (UNITE) model. UNITE provides accurate disease risk prediction and uncertainty estimation leveraging multi-sourced health data. We evaluate UNITE on real-world disease risk prediction tasks: nonalcoholic fatty liver disease (NASH) and Alzheimer's disease (AD) UNITE achieves up to 0.841 in F1 score for AD detection, up to 0.609 in PR-AUC for NASH detection, and outperforms various state-of-the-art baselines by up to $19%$ over the best baseline.
  arXiv  Detail & Related papers  (2020-10-22T02:28:11Z)
• Robustness to Spurious Correlations via Human Annotations [100.63051542531171]
  We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality. Specifically, we use human annotation to augment each training example with a potential unmeasured variable. We then introduce a new distributionally robust optimization objective over unmeasured variables (UV-DRO) to control the worst-case loss over possible test-time shifts.
  arXiv  Detail & Related papers  (2020-07-13T20:05:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.