  The COVID-19 pandemic has caused devastating economic and social disruption,
straining the resources of healthcare institutions worldwide. This has led to a
nationwide call for models to predict hospitalization and severe illness in
patients with COVID-19 to inform distribution of limited healthcare resources.
We respond to one of these calls specific to the pediatric population. To
address this challenge, we study two prediction tasks for the pediatric
population using electronic health records: 1) predicting which children are
more likely to be hospitalized, and 2) among hospitalized children, which
individuals are more likely to develop severe symptoms.
  We respond to the national Pediatric COVID-19 data challenge with a novel
machine learning model, MedML. MedML extracts the most predictive features
based on medical knowledge and propensity scores from over 6 million medical
concepts and incorporates the inter-feature relationships between heterogeneous
medical features via graph neural networks (GNN). We evaluate MedML across
143,605 patients for the hospitalization prediction task and 11,465 patients
for the severity prediction task using data from the National Cohort
Collaborative (N3C) dataset. We also report detailed group-level and
individual-level feature importance analyses to evaluate the model
interpretability.
  MedML achieves up to a 7% higher AUROC score and up to a 14% higher AUPRC
score compared to the best baseline machine learning models and performs well
across all nine national geographic regions and over all three-month spans
since the start of the pandemic. Our cross-disciplinary research team has
developed a method of incorporating clinical domain knowledge as the framework
for a new type of machine learning model that is more predictive and
explainable than current state-of-the-art data-driven feature selection
methods.
