An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation
Journal:
arXiv
Published Date:
Dec 12, 2024
Abstract
Reliable prediction of pediatric obesity can offer a valuable resource to
providers, helping them engage in timely preventive interventions before the
disease is established. Many efforts have been made to develop ML-based
predictive models of obesity, and some studies have reported high predictive
performances. However, no commonly used clinical decision support tool based on
existing ML models currently exists. This study presents a novel end-to-end
pipeline specifically designed for pediatric obesity prediction, which supports
the entire process of data extraction, inference, and communication via an API
or a user interface. While focusing only on routinely recorded data in
pediatric electronic health records (EHRs), our pipeline uses a diverse
expert-curated list of medical concepts to predict the 1-3 years risk of
developing obesity. Furthermore, by using the Fast Healthcare Interoperability
Resources (FHIR) standard in our design procedure, we specifically target
facilitating low-effort integration of our pipeline with different EHR systems.
In our experiments, we report the effectiveness of the predictive model as well
as its alignment with the feedback from various stakeholders, including ML
scientists, providers, health IT personnel, health administration
representatives, and patient group representatives.
