An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation

TL;DR

This paper introduces an end-to-end, interoperable machine learning pipeline designed for pediatric obesity risk prediction, integrating data extraction, inference, and communication to support clinical decision-making.

Contribution

It presents a novel pipeline that leverages FHIR standards and routinely recorded EHR data for accurate, easily integrable pediatric obesity risk prediction.

Findings

Effective predictive performance demonstrated

Pipeline aligns well with stakeholder feedback

Supports seamless integration with various EHR systems

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.