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.

研究了两种用于早期检测医院获得性感染的模型：感染风险指数 (IRI) 和呼吸机相关性肺炎 (VAP) 预测模型，在比较分析这两种模型的基础上，得出了并行疾病特定模型的部署方法。