Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa.
Machine learning algorithms for predicting determinants of COVID-19 mortality in South Africa.
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DOI：
10.3389/frai.2023.1171256
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发表时间：
2023
影响因子：
4
通讯作者：
Nyasulu, Peter S.
中科院分区：
文献类型：
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作者：
Chimbunde, Emmanuel;Sigwadhi, Lovemore N.;Tamuzi, Jacques L.;Okango, Elphas L.;Daramola, Olawande;Ngah, Veranyuy D.;Nyasulu, Peter S.
COVID-19使医疗资源紧张，需要有效的预测来有效地对患者进行分类。本研究基于机器学习算法量化了南非COVID-19危险因素并预测了COVID-19重症监护病房（ICU）死亡率。本研究的数据来自于2020年3月26日至2021年2月10日期间入组的392名COVID-19 ICU患者。我们使用人工神经网络（ANN）和随机森林（RF）来预测ICU患者的死亡率，并使用包含9个协变量的半参数逻辑回归，其中包括基于K-means聚类的分组变量。使用敏感性、准确性、特异性和Cohen’s K统计量对算法进行进一步评估。从半参数logistic回归和ANN变量重要性来看，年龄、性别、集群、是否存在严重症状、是否使用呼吸机和哮喘合并症对ICU死亡有显著影响。特别是，哮喘患者的死亡率比非哮喘患者高6倍。在单变量和多变量回归中，高龄、PF1和2、FiO2、严重症状、哮喘、血氧饱和度和聚类4是死亡率的强预测因子。RF模型显示，插管状态、年龄、群集、糖尿病和高血压是死亡率的前五大显著预测因素。人工神经网络的准确率为71%，精密度为83%，F1得分为100%，马修相关系数（MCC）得分为100%，召回率为88%。此外，Cohen的k值为0.75验证了ANN的最极端判别能力。相比之下，RF模型提供了76%的召回率，87%的精度和65%的MCC。基于这些发现，我们可以得出结论，ANN和RF都可以准确预测ICU的COVID-19死亡率。所建立的模型能够准确预测COVID-19患者诊断后的预后。该模型可用于在资源受限的icu中优先考虑高死亡风险的COVID-19患者。
COVID-19 has strained healthcare resources, necessitating efficient prognostication to triage patients effectively. This study quantified COVID-19 risk factors and predicted COVID-19 intensive care unit (ICU) mortality in South Africa based on machine learning algorithms. Data for this study were obtained from 392 COVID-19 ICU patients enrolled between 26 March 2020 and 10 February 2021. We used an artificial neural network (ANN) and random forest (RF) to predict mortality among ICU patients and a semi-parametric logistic regression with nine covariates, including a grouping variable based on K-means clustering. Further evaluation of the algorithms was performed using sensitivity, accuracy, specificity, and Cohen's K statistics. From the semi-parametric logistic regression and ANN variable importance, age, gender, cluster, presence of severe symptoms, being on the ventilator, and comorbidities of asthma significantly contributed to ICU death. In particular, the odds of mortality were six times higher among asthmatic patients than non-asthmatic patients. In univariable and multivariate regression, advanced age, PF1 and 2, FiO2, severe symptoms, asthma, oxygen saturation, and cluster 4 were strongly predictive of mortality. The RF model revealed that intubation status, age, cluster, diabetes, and hypertension were the top five significant predictors of mortality. The ANN performed well with an accuracy of 71%, a precision of 83%, an F1 score of 100%, Matthew's correlation coefficient (MCC) score of 100%, and a recall of 88%. In addition, Cohen's k-value of 0.75 verified the most extreme discriminative power of the ANN. In comparison, the RF model provided a 76% recall, an 87% precision, and a 65% MCC. Based on the findings, we can conclude that both ANN and RF can predict COVID-19 mortality in the ICU with accuracy. The proposed models accurately predict the prognosis of COVID-19 patients after diagnosis. The models can be used to prioritize COVID-19 patients with a high mortality risk in resource-constrained ICUs.
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影响因子：
3.9
作者：
Al Oweidat K;Al-Amer R;Saleh MY;Albtoosh AS;Toubasi AA;Ribie MK;Hasuneh MM;Alfaqheri DL;Alshurafa AH;Ribie M;Ali AM;Obeidat N
通讯作者：
Obeidat N
影响因子：
3.5
作者：
Moulaei K;Shanbehzadeh M;Mohammadi-Taghiabad Z;Kazemi-Arpanahi H
通讯作者：
Kazemi-Arpanahi H
影响因子：
8.4
作者：
Shen, Christopher Y.
通讯作者：
Shen, Christopher Y.
影响因子：
15.2
作者：
Subudhi S;Verma A;Patel AB;Hardin CC;Khandekar MJ;Lee H;McEvoy D;Stylianopoulos T;Munn LL;Dutta S;Jain RK
通讯作者：
Jain RK
影响因子：
4.6
作者：
通讯作者：
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