Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.
Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.
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DOI：
10.7717/peerj.10083
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发表时间：
2020
期刊：
影响因子：
2.7
通讯作者：
Saraswathy Gopalan S
中科院分区：
文献类型：
--
作者：
Das AK;Mishra S;Saraswathy Gopalan S
最近的新冠肺炎大流行已成为对全球卫生安全的威胁。使用机器学习的CoVID-19预测模型很少。利用机器学习预测韩国新冠肺炎确诊患者的死亡率，并将表现最好的算法部署为开源的在线预测决策工具。使用五种机器学习算法（逻辑回归、支持向量机、K近邻、随机森林和梯度增强）预测2020年1月20日至2020年5月30日确诊CoVID-19患者（n = 3524）的死亡率。比较了算法的性能，并将性能最好的算法部署为在线预测工具。logistic回归算法在识别（ROC曲线下面积= 0.830）、校准（Matthews相关系数= 0.433；Brier评分= 0.036）和。将表现最佳的算法（逻辑回归）部署为在线CoVID-19社区死亡风险预测工具CoCoMoRP（）。我们描述了一个开源机器学习工具的开发和部署，该工具使用公开的监测数据来预测CoVID-19确诊患者的死亡风险。除其他方法外，卫生服务提供者和政策制定者等潜在利益攸关方可利用这一工具在社区一级对患者进行分类。
The recent pandemic of CoVID-19 has emerged as a threat to global health security. There are very few prognostic models on CoVID-19 using machine learning. To predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. Mortality for confirmed CoVID-19 patients (n = 3,524) between January 20, 2020 and May 30, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). The performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool. The logistic regression algorithm was the best performer in terms of discrimination (area under ROC curve = 0.830), calibration (Matthews Correlation Coefficient = 0.433; Brier Score = 0.036) and. The best performing algorithm (logistic regression) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (). We describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policymakers to triage patients at the community level in addition to other approaches.
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影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
168.9
作者：
Chen, Huijun;Guo, Juanjuan;Zhang, Yuanzhen
通讯作者：
Zhang, Yuanzhen
DOI：
10.1056/nejmp1702071
发表时间：
2017-06-29
期刊：
The New England journal of medicine
影响因子：
--
作者：
Chen JH;Asch SM
通讯作者：
Asch SM
影响因子：
--
作者：
Raeisi Shahraki H;Pourahmad S;Zare N
通讯作者：
Zare N
影响因子：
4.4
作者：
Chicco, Davide;Jurman, Giuseppe
通讯作者：
Jurman, Giuseppe