Mukhamediya Azamat, Arupzhanov Iliyar, Zollanvari Amin, Zhumambayeva Saule, Nadyrov Kamalzhan, Khamidullina Zaituna, Tazhibayeva Karina, Myrzabekova Aigul, Jaxalykova Kulyash K, Terzic Milan, Bapayeva Gauri, Kulbayeva Saltanat, Abuova Gulzhan Narkenovna, Erezhepov Baktigali Aubayevich, Sarbalina Asselzhan, Sipenova Aigerim, Mukhtarova Kymbat, Ghahramany Ghazal, Sarria-Santamera Antonio
Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan.
Astana Medical University, Astana 010000, Kazakhstan.
J Clin Med. 2024 Dec 17;13(24):7705. doi: 10.3390/jcm13247705.
: The rapid onset of COVID-19 placed immense strain on many already overstretched healthcare systems. The unique physiological changes in pregnancy, amplified by the complex effects of COVID-19 in pregnant women, rendered prioritization of infected expectant mothers more challenging. This work aims to use state-of-the-art machine learning techniques to predict whether a COVID-19-infected pregnant woman will be admitted to ICU (Intensive Care Unit). : A retrospective study using data from COVID-19-infected women admitted to one hospital in Astana and one in Shymkent, Kazakhstan, from May to July 2021. The developed machine learning platform implements and compares the performance of eight binary classifiers, including Gaussian naïve Bayes, K-nearest neighbors, logistic regression with regularization, random forest, AdaBoost, gradient boosting, eXtreme gradient boosting, and linear discriminant analysis. : Data from 1292 pregnant women with COVID-19 were analyzed. Of them, 10.4% were admitted to ICU. Logistic regression with regularization achieved the highest -score during the model selection phase while achieving an AUC of 0.84 on the test set during the evaluation stage. Furthermore, the feature importance analysis conducted by calculating Shapley Additive Explanation values points to leucocyte counts, C-reactive protein, pregnancy week, and eGFR and hemoglobin as the most important features for predicting ICU admission. : The predictive model obtained here may be an efficient support tool for prioritizing care of COVID-19-infected pregnant women in clinical practice.
新型冠状病毒肺炎（COVID-19）的迅速爆发给许多本就不堪重负的医疗系统带来了巨大压力。孕期独特的生理变化，再加上COVID-19对孕妇的复杂影响，使得对感染的准妈妈进行优先级排序更具挑战性。这项工作旨在使用最先进的机器学习技术来预测感染COVID-19的孕妇是否会被收入重症监护病房（ICU）。
一项回顾性研究，使用了2021年5月至7月在哈萨克斯坦阿斯塔纳的一家医院和奇姆肯特的一家医院收治的感染COVID-19妇女的数据。所开发的机器学习平台实现并比较了八个二分类器的性能，包括高斯朴素贝叶斯、K近邻、带正则化的逻辑回归、随机森林、自适应增强、梯度提升、极端梯度提升和线性判别分析。
对1292名感染COVID-19的孕妇的数据进行了分析。其中，10.4%的孕妇被收入ICU。在模型选择阶段，带正则化的逻辑回归获得了最高分，在评估阶段，其在测试集上的曲线下面积（AUC）为0.84。此外，通过计算夏普利值进行的特征重要性分析表明，白细胞计数、C反应蛋白、孕周、估算肾小球滤过率（eGFR）和血红蛋白是预测ICU收治的最重要特征。
此处获得的预测模型可能是临床实践中对感染COVID-19的孕妇进行护理优先级排序的有效支持工具。
