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使用临床数据进行COVID-19生存分析和出院时间可能性预测的机器学习方法

Machine-Learning Approaches in COVID-19 Survival Analysis and Discharge-Time Likelihood Prediction Using Clinical Data.

作者信息

Nemati Mohammadreza, Ansary Jamal, Nemati Nazafarin

机构信息

Electrical Engineering and Computer Science, University of Toledo, Toledo, OH, USA.

Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, OH, USA.

出版信息

Patterns (N Y). 2020 Aug 14;1(5):100074. doi: 10.1016/j.patter.2020.100074. Epub 2020 Jul 4.

Abstract

As a highly contagious respiratory disease, COVID-19 has yielded high mortality rates since its emergence in December 2019. As the number of COVID-19 cases soars in epicenters, health officials are warning about the possibility of the designated treatment centers being overwhelmed by coronavirus patients. In this study, several computational techniques are implemented to analyze the survival characteristics of 1,182 patients. The computational results agree with the outcome reported in early clinical reports released for a group of patients from China that confirmed a higher mortality rate in men compared with women and in older age groups. The discharge-time prediction of COVID-19 patients was also evaluated using different machine-learning and statistical analysis methods. The results indicate that the Gradient Boosting survival model outperforms other models for patient survival prediction in this study. This research study is aimed to help health officials make more educated decisions during the outbreak.

摘要

作为一种高度传染性的呼吸道疾病,自2019年12月出现以来,新冠病毒肺炎已导致高死亡率。随着新冠病毒肺炎病例在疫情中心急剧增加,卫生官员警告称,指定的治疗中心有可能被新冠病毒患者挤满。在本研究中,采用了几种计算技术来分析1182名患者的生存特征。计算结果与早期针对一组中国患者发布的临床报告结果一致,该报告证实男性的死亡率高于女性,且老年人群的死亡率更高。还使用不同的机器学习和统计分析方法对新冠病毒肺炎患者的出院时间预测进行了评估。结果表明,在本研究中,梯度提升生存模型在患者生存预测方面优于其他模型。这项研究旨在帮助卫生官员在疫情爆发期间做出更明智的决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/38a3/7660394/b37d3a775dc1/fx1.jpg

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