Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.
Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.
复制标题
机器学习方法以识别患者合并症和症状增加COVID-19的死亡风险。
DOI：
10.3390/diagnostics11081383
复制
发表时间：
2021-07-31
期刊：
影响因子：
--
通讯作者：
Moni MA
中科院分区：
文献类型：
--
作者：
Aktar S;Talukder A;Ahamad MM;Kamal AHM;Khan JR;Protikuzzaman M;Hossain N;Azad AKM;Quinn JMW;Summers MA;Liaw T;Eapen V;Moni MA
为COVID-19患者提供适当护理是一项重大的全球挑战。COVID-19是由大流行的SARS-CoV-2病毒引起的疾病。许多感染者可能已有疾病，这些疾病可能与COVID-19相互作用，增加症状严重程度和死亡风险。COVID-19患者合并症可能提供有关个人严重疾病和死亡风险的信息。因此，确定合并症与严重症状和死亡率的关联程度将极大地有助于COVID-19的护理规划和提供。为了评估这一点，我们对已发表的全球文献进行了荟萃分析，并使用汇总的COVID-19全球数据集进行了机器学习预测分析。我们的荟萃分析显示，在目前已发表的文献中，慢性阻塞性肺疾病（COPD）、脑血管疾病（CEVD）、心血管疾病（CVD）、2型糖尿病、恶性肿瘤和高血压与COVID-19严重程度的相关性最为显著。使用新的汇总队列数据的机器学习分类同样发现，COPD、CVD、CKD、2型糖尿病、恶性肿瘤、高血压以及哮喘是对死者与COVID-19幸存者进行分类的最重要特征。虽然年龄和性别是死亡率的最重要预测因素，但就症状合并症组合而言，观察到肺炎-高血压、肺炎-糖尿病和急性呼吸窘迫综合征(ARDS) -高血压与COVID-19死亡率的相关性最显著。这些结果突出了最有可能面临与covid -19相关的严重发病率和死亡率风险的患者群体，这对医院资源的优先次序产生了影响。
Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resources.
登录
查看更多内容
影响因子：
6.1
作者：
Liu, Kui;Fang, Yuan-Yuan;Liu, Hui-Guo
通讯作者：
Liu, Hui-Guo
DOI：
10.1164/rccm.202002-0445oc
发表时间：
2020-06-01
影响因子：
24.7
作者：
Feng, Yun;Ling, Yun;Qu, Jieming
通讯作者：
Qu, Jieming
影响因子：
24.3
作者：
Guan, Wei-jie;Liang, Wen-hua;He, Jian-xing
通讯作者：
He, Jian-xing
影响因子：
11.8
作者：
Ji, Dong;Zhang, Dawei;Qin, Enqiang
通讯作者：
Qin, Enqiang
影响因子：
6.1
作者：
Liu Wei;Tao Zhao-Wu;Hu Yi
通讯作者：
Hu Yi