Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.
Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.
复制标题
DOI：
10.1186/s12911-020-01266-z
复制
发表时间：
2020-09-29
影响因子：
3.5
通讯作者：
Ongkeko WM
中科院分区：
文献类型：
--
作者：
Li WT;Ma J;Shende N;Castaneda G;Chakladar J;Tsai JC;Apostol L;Honda CO;Xu J;Wong LM;Zhang T;Lee A;Gnanasekar A;Honda TK;Kuo SZ;Yu MA;Chang EY;Rajasekaran MR;Ongkeko WM
最近的2019冠状病毒病（COVID-19）大流行给全球医疗保健系统带来了严重压力，而COVID-19检测的严重短缺加剧了这种压力。在这项研究中，我们建议通过应用机器学习重新分析来自151项已发表研究的COVID-19数据，基于患者症状和常规测试结果生成更准确的COVID-19诊断模型。我们的目标是调查临床变量之间的相关性，将COVID-19患者聚类为亚型，并生成一个计算分类模型，用于仅基于临床变量区分COVID-19患者和流感患者。我们发现了临床变量之间的一些新的关联，包括男性与血清淋巴细胞和中性粒细胞水平较高之间的相关性。我们发现，COVID-19患者可以根据免疫细胞的血清水平、性别和报告的症状分为不同的亚型。最后，我们训练了一个XGBoost模型，以实现92.5%的灵敏度和97.9%的特异性来区分COVID-19患者和流感患者。我们证明，在大型临床数据集上训练的计算方法可以产生更准确的COVID-19诊断模型，以减轻缺乏测试的影响。我们还介绍了先前未知的COVID-19临床变量相关性和临床亚组。
The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.
登录
查看更多内容
影响因子：
39.3
作者：
Al'Arefilb, Subhi J.;Maliakal, Gabriel;Shaw, Leslee J.
通讯作者：
Shaw, Leslee J.
影响因子：
3.1
作者：
Li, Yuanzhe;Guo, FeiFei;Guo, YanJun
通讯作者：
Guo, YanJun
影响因子：
12.7
作者：
Sun, Pengfei;Qie, Shuyan;Xi, Jianing
通讯作者：
Xi, Jianing
影响因子：
4.4
作者：
Squires RB;Noronha J;Hunt V;García-Sastre A;Macken C;Baumgarth N;Suarez D;Pickett BE;Zhang Y;Larsen CN;Ramsey A;Zhou L;Zaremba S;Kumar S;Deitrich J;Klem E;Scheuermann RH
通讯作者：
Scheuermann RH
影响因子：
7.5
作者：
Jerez, Jose M.;Molina, Ignacio;Franco, Leonardo
通讯作者：
Franco, Leonardo