An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

TL;DR

This study presents an AI system combining deep learning and gradient boosting to predict COVID-19 patient deterioration within 96 hours, aiding emergency triage with high accuracy and interpretability, validated in real clinical settings.

Contribution

The paper introduces a novel AI prognosis system integrating chest X-ray analysis and clinical data, achieving high predictive accuracy and real-time deployment during the pandemic.

Findings

Achieved an AUC of 0.786 for deterioration prediction within 96 hours.

Deep neural network performs comparably to radiologists in image interpretation.

System successfully deployed in real-time clinical setting during the pandemic.

Abstract

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.