An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
This addresses the need for rapid triage of COVID-19 patients in emergency settings, though it is incremental as it applies existing AI methods to a specific clinical problem.
The authors tackled the problem of predicting COVID-19 patient deterioration in emergency departments by developing an AI system that combines deep neural networks on chest X-rays and gradient boosting on clinical variables, achieving an AUC of 0.786 for predicting deterioration within 96 hours using data from 3,661 patients.
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.