Zoabi Yazeed, Deri-Rozov Shira, Shomron Noam
Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.
NPJ Digit Med. 2021 Jan 4;4(1):3. doi: 10.1038/s41746-020-00372-6.
Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.
对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）进行有效的筛查能够实现对2019冠状病毒病（COVID-19）的快速高效诊断，并减轻医疗系统的负担。已经开发出了结合多种特征来估计感染风险的预测模型。这些模型旨在协助全球医护人员对患者进行分流，尤其是在医疗资源有限的情况下。我们建立了一种机器学习方法，该方法基于51831名接受检测的个体（其中4769人确诊感染COVID-19）的记录进行训练。测试集包含接下来一周的数据（47401名接受检测的个体，其中3624人确诊感染COVID-19）。我们的模型仅使用八个二元特征就能高精度地预测COVID-19检测结果：性别、年龄≥60岁、已知与感染个体有接触，以及五种初始临床症状的出现情况。总体而言，基于以色列卫生部公开报告的全国数据，我们开发了一种模型，该模型通过询问基本问题获取的简单特征来检测COVID-19病例。在其他考量因素中，我们的框架可用于在检测资源有限时对COVID-19检测进行优先级排序。
