论文标题
一个人工智能系统，用于预测急诊室中Covid-19患者的恶化
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
论文作者
论文摘要
在2019年冠状病毒病（Covid-19）大流行期间，急诊室的患者快速而准确的分类对于为决策提供了重要的分类至关重要。我们建议使用深层神经网络自动预测劣化风险的数据驱动方法，该网络从胸部X射线图像中学习，并从常规的临床变量中学习梯度增强模型。我们的AI预后系统使用来自3,661名患者的数据训练，在96小时内预测恶化时，在接收器操作特征曲线（AUC）下达到了0.786（95％CI：0.745-0.830）的区域。深度神经网络提取物在一项读者研究中，胸部X射线图像的信息范围X射线图像的信息领域可帮助临床医生解释预测并与两名放射科医生的表现相当。为了在真正的临床环境中验证性能，我们在大流行的第一波浪潮中默默地部署了纽约大学Langone Health的深神经网络的初步版本，这实时产生了准确的预测。总而言之，我们的发现证明了拟议系统的潜力，即协助前线医生参加COVID-19患者的分类。
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.
