COVID-19 patient triaging with predictive outcome of the patients upon first present to emergency department (ED) is crucial for improving patient prognosis, as well as better hospital resources management and cross-infection control. We trained a deep feature fusion model to predict patient outcomes, where the model inputs were EHR data including demographic information, co-morbidities, vital signs and laboratory measurements, plus patient's CXR images. The model output was patient outcomes defined as the most insensitive oxygen therapy required. For patients without CXR images, we employed Random Forest method for the prediction. Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance. The study's dataset (the "MGB COVID Cohort") was constructed from all patients presenting to the Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020. ED visits with incomplete or erroneous data were excluded. Patients with no test order for COVID or confirmed negative test results were excluded. Patients under the age of 15 were also excluded. Finally, electronic health record (EHR) data from a total of 11060 COVID-19 confirmed or suspected patients were used in this study. Chest X-ray (CXR) images were also collected from each patient if available. Results show that CO-RISK score achieved area under the Curve (AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and 0.92 in 72 hours on the testing dataset. The model shows superior performance to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with physician's decisions, CO-RISK score has demonstrated superior performance to human in making ICU/floor decisions.

翻译：COVID-19 患者先到急诊部(ED),先预测病人的预测结果,然后进行病人的预测结果。 对于没有 CXR 图像的病人,我们使用随机森林方法进行预测。COVID-19 严重结果的预测风险分数(CO-RISK 评分)来自模型输出,预测病人的结果,通过测试数据集对模型输入为 EHR 数据,包括人口信息、共同发病率、生命迹象和实验室测量,加上病人的CXXR 图像。模型输出结果被定义为最不敏感的氧治疗。对于没有 CXRR 图像的病人,我们使用随机森林方法进行预测。COVID-19 严重结果的预测风险分数(CO-RISK 评分)来自模型输出,在测试数据集中进行了评价,在测试中,从所有病人(MGBCO COVID) 数据从3月1日至6月1日(MGBSt) 健康系统(MGB) 显示最不敏感的诊断结果。(CRBRRR) 访问不完全或错误数据。(CO-RISD-19的测算数据显示,在C 15年的测算数据中也显示,在C 15年的测算数据中显示。