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A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with

Louis Ehwerhemuepha1,2, Sidy Danioko2, Shiva Verma3

  • 1Children's Hospital of Orange County, Orange, CA, 92868, United States.

Intelligence-Based Medicine
|March 22, 2021
PubMed

Insights

This study developed a super learner ensemble model to predict COVID-19 severity in adults with cardiovascular disease, achieving high predictive accuracy and outperforming individual models.

Area of Science:

  • Computational biology and bioinformatics
  • Epidemiology and public health
  • Machine learning in healthcare

Background:

  • Cardiovascular diseases are linked to increased COVID-19 severity in adults.
  • Accurate prediction of COVID-19 severity is crucial for patient management.

Purpose of the Study:

  • To develop and evaluate a super learner ensemble model for predicting COVID-19 severity.
  • To assess the predictive performance of machine learning models in patients with cardiovascular conditions.

Main Methods:

  • Utilized the Cerner Real-World Data COVID-19 Dataset, including 33,042 adult patients with cardiovascular diseases.
  • Developed 14 statistical and machine learning models, combined into a super learner ensemble.
  • Evaluated model performance using cross-validated AUROC and an independent test set.

Main Results:

  • Individual models like LASSO regression and extreme gradient boosting showed strong predictive power (AUROC ~0.796).
  • The super learner ensemble model achieved a cross-validated AUROC of 0.8006.
  • The unbiased AUROC on an independent test set was 0.8057, demonstrating robust performance.

Conclusions:

  • Highly predictive models for COVID-19 severity can be effectively built for patients with cardiovascular conditions.
  • Super learning ensemble models significantly enhance predictive accuracy compared to individual and classical ensemble methods.
Abstract

Keywords:
COVID-19COVID-19 severityCardiovascular conditionsEnsemble learningPredicting COVID-19 severitySuper learning

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