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.
@article{arxiv.2008.01774,
title = {An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department},
author = {Farah E. Shamout and Yiqiu Shen and Nan Wu and Aakash Kaku and Jungkyu Park and Taro Makino and Stanisław Jastrzębski and Jan Witowski and Duo Wang and Ben Zhang and Siddhant Dogra and Meng Cao and Narges Razavian and David Kudlowitz and Lea Azour and William Moore and Yvonne W. Lui and Yindalon Aphinyanaphongs and Carlos Fernandez-Granda and Krzysztof J. Geras},
journal= {arXiv preprint arXiv:2008.01774},
year = {2020}
}