  During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate
triage of patients at the emergency department is critical to inform
decision-making. We propose a data-driven approach for automatic prediction of
deterioration risk using a deep neural network that learns from chest X-ray
images and a gradient boosting model that learns from routine clinical
variables. Our AI prognosis system, trained using data from 3,661 patients,
achieves an area under the receiver operating characteristic curve (AUC) of
0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The
deep neural network extracts informative areas of chest X-ray images to assist
clinicians in interpreting the predictions and performs comparably to two
radiologists in a reader study. In order to verify performance in a real
clinical setting, we silently deployed a preliminary version of the deep neural
network at New York University Langone Health during the first wave of the
pandemic, which produced accurate predictions in real-time. In summary, our
findings demonstrate the potential of the proposed system for assisting
front-line physicians in the triage of COVID-19 patients.
