Comparing machine learning algorithms for predicting COVID-19 mortality.
Comparing machine learning algorithms for predicting COVID-19 mortality.
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DOI：
10.1186/s12911-021-01742-0
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发表时间：
2022-01-04
影响因子：
3.5
通讯作者：
Kazemi-Arpanahi H
中科院分区：
文献类型：
--
作者：
Moulaei K;Shanbehzadeh M;Mohammadi-Taghiabad Z;Kazemi-Arpanahi H
冠状病毒病(新冠肺炎)住院患者总是有死亡的风险。机器学习(ML)算法可以作为预测新冠肺炎住院患者死亡率的潜在解决方案。因此，我们的研究旨在比较几种利用患者首次入院时的数据预测新冠肺炎死亡率的ML算法，并选择性能最好的算法作为预测决策的工具。在这项研究中，经过特征选择，基于确认的预测因子，提取了从伊朗阿巴丹市阿亚图拉·塔雷加尼医院登记处获得的1500名符合条件的患者(1386名幸存者和144名死亡)的信息。之后，训练了几个最大似然算法来预测新冠肺炎死亡率。最后，为了评估模型的性能，计算了从混淆矩阵得到的度量。研究参与者为1500名患者；发现男性人数高于女性(836人对664人)，年龄中值为57.25岁(四分位数为18-100岁)。在进行特征选择后，在38个特征中，呼吸困难、ICU入院和氧疗被发现是前三个预测因素。吸烟、丙氨酸氨基转移酶和血小板计数被发现是新冠肺炎死亡率的三个最低预测因子。实验结果表明，随机森林算法的准确率为95.03%，敏感度为90.70%，精确度为94.23%，特异度为95.10%，接收者操作特征(ROC)为99.02%。研究发现，ML在预测新冠肺炎死亡率方面具有合理的准确性。因此，基于ML的预测模型，特别是RF算法，可能有助于识别死亡风险较高的患者，并为临床医生提供适当的干预措施。
The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient’s data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models’ performance, the metrics derived from the confusion matrix were calculated. The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18–100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.
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基于机器学习的临床决策支持系统，用于早期Covid-19死亡率预测。
影响因子：
5.2
作者：
Karthikeyan A;Garg A;Vinod PK;Priyakumar UD
通讯作者：
Priyakumar UD
基于机器学习的早期预警系统能够准确预测 COVID-19 的死亡风险
影响因子：
16.6
作者：
Gao Y;Cai GY;Fang W;Li HY;Wang SY;Chen L;Yu Y;Liu D;Xu S;Cui PF;Zeng SQ;Feng XX;Yu RD;Wang Y;Yuan Y;Jiao XF;Chi JH;Liu JH;Li RY;Zheng X;Song CY;Jin N;Gong WJ;Liu XY;Huang L;Tian X;Li L;Xing H;Ma D;Li CR;Ye F;Gao QL
通讯作者：
Gao QL
影响因子：
5.8
作者：
Molinaro, AM;Simon, R;Pfeiffer, RM
通讯作者：
Pfeiffer, RM
DOI：
10.1038/s41379-020-00700-x
发表时间：
2021-03
期刊：
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
影响因子：
--
作者：
Booth AL;Abels E;McCaffrey P
通讯作者：
McCaffrey P
影响因子：
2.7
作者：
Das AK;Mishra S;Saraswathy Gopalan S
通讯作者：
Saraswathy Gopalan S