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

Azamat Mukhamediya¹, Iliyar Arupzhanov¹, Amin Zollanvari¹

¹Department of Electrical and Computer Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan.

Journal of Clinical Medicine

|January 8, 2025

Summary

This summary is machine-generated.

Machine learning models can predict intensive care unit (ICU) admission for pregnant women with COVID-19. Key predictors include leucocyte count, C-reactive protein, and pregnancy week, aiding clinical care prioritization.

Keywords:

COVID-19 feature importance intensive care unit admission machine learning pregnancy

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Area of Science:

Medical Informatics
Public Health
Obstetrics

Background:

COVID-19 significantly strained healthcare systems globally.
Pregnancy presents unique physiological challenges, complicated by COVID-19 infection.
Prioritizing care for pregnant COVID-19 patients required effective predictive tools.

Purpose of the Study:

To develop and evaluate machine learning models for predicting ICU admission in pregnant women with COVID-19.
To identify key clinical features associated with severe COVID-19 outcomes in pregnancy.

Main Methods:

Retrospective study of 1292 pregnant women with COVID-19 in Kazakhstan (May-July 2021).
Comparison of eight binary classifiers including logistic regression, random forest, and gradient boosting.
Feature importance analysis using Shapley Additive Explanation (SHAP) values.

Main Results:

10.4% of analyzed pregnant women were admitted to the ICU.
Logistic regression with L regularization achieved the highest F1-score and an AUC of 0.84.
Leucocyte count, C-reactive protein, pregnancy week, eGFR, and hemoglobin were critical predictors.

Conclusions:

A predictive model using machine learning can effectively support clinical decision-making.
This tool aids in prioritizing the care of pregnant women with COVID-19 requiring intensive care.
Early identification of high-risk pregnancies can optimize resource allocation in healthcare settings.