Predicting Intensive Care Unit Admission in COVID-19 Infected Pregnant Women Using Machine Learning

Background: The rapid onset of COVID-19 placed immense strain on many already overstretched healthcare systems. The unique physiological changes of 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). Methods: A retrospective study using data from COVID-19 infected women admitted to 2 hospital 1 in Astana and 1 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 L2 regularization, random forest, AdaBoost, gradient boosting, eXtreme gradient boosting, and linear discriminant analysis. Results: Data from 1168 pregnant women with COVID-19 was analyzed. From them, 9.4% were admitted to ICU. Logistic regression with L2 regularization achieved the highest F1-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. Conclusion: The predictive model here obtained may be an efficient support tool for prioritizing care of COVID-19 infected pregnant women in clinical practice.