A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions - PubMed (original) (raw)

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

Louis Ehwerhemuepha et al. Intell Based Med. 2021.

Abstract

Background: Cardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients.

Method: The COVID-19 Dataset of the Cerner Real-World Data was used for this study. Data on adult patients (18 years or older) with cardiovascular diseases between 2017 and 2019 were retrieved and a total of 13 of these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a more powerful super learning model for predicting COVID-19 severity on admission to the hospital.

Result: LASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159).

Conclusion: Highly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.

Keywords: COVID-19; COVID-19 severity; Cardiovascular conditions; Ensemble learning; Predicting COVID-19 severity; Super learning.

© 2021 The Author(s).

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1

Fig. 1

Visual description of super learning by van der Laan and Rose (2011, 2018).

Fig. 2

Fig. 2

The precision-recall curve for the Super Learner model.

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