A Comparative Analysis of Machine Learning Models for Early Detection of
  Hospital-Acquired Infections
Ethan Harvey Junzi Dong Erina Ghosh Ali Samadani
As more and more infection-specific machine learning models are developed and
planned for clinical deployment, simultaneously running predictions from
different models may provide overlapping or even conflicting information. It is
important to understand the concordance and behavior of parallel models in
deployment. In this study, we focus on two models for the early detection of
hospital-acquired infections (HAIs): 1) the Infection Risk Index (IRI) and 2)
the Ventilator-Associated Pneumonia (VAP) prediction model. The IRI model was
built to predict all HAIs, whereas the VAP model identifies patients at risk of
developing ventilator-associated pneumonia. These models could make important
improvements in patient outcomes and hospital management of infections through
early detection of infections and in turn, enable early interventions. The two
models vary in terms of infection label definition, cohort selection, and
prediction schema. In this work, we present a comparative analysis between the
two models to characterize concordances and confusions in predicting HAIs by
these models. The learnings from this study will provide important findings for
how to deploy multiple concurrent disease-specific models in the future.