Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19

Hierarchical ML models trained on vital signs, laboratory results, and demographic data show promise for the prediction of MV duration, ECMO use, and mortality in COVID-19 patients.

Authors

Erin Hedlund-Botti (ORCID: https://orcid.org/0000-0001-9890-4873)
Matthew Gombolay (ORCID: https://orcid.org/0000-0002-5321-6038)
Nina Moorman (ORCID: https://orcid.org/0000-0003-2697-1318)
Grace Gombolay (ORCID: https://orcid.org/0000-0003-4830-7792)

Institutions

Georgia Institute of Technology (US)
Emory University (US)
Children's Healthcare of Atlanta (US)

Publication Details

Journal: Frontiers in Artificial Intelligence
Published: 2026-01-06
DOI: https://doi.org/10.3389/frai.2025.1661637
Primary Topic: Mechanical Circulatory Support Devices
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Georgia Institute of Technology
Emory University

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article

Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19

Erin Hedlund-Botti, Matthew Gombolay, Nina Moorman, Grace Gombolay

Jan 6, 2026

Frontiers in Artificial Intelligence

Mechanical Circulatory Support Devices

article

Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19

Erin Hedlund-Botti, Matthew Gombolay, Nina Moorman, Grace Gombolay

January 6, 2026article en

Abstract

Hierarchical ML models trained on vital signs, laboratory results, and demographic data show promise for the prediction of MV duration, ECMO use, and mortality in COVID-19 patients.

Frontiers in Artificial IntelligenceVol. 8

Georgia Institute of Technology (US), Emory University (US), Children's Healthcare of Atlanta (US)

Georgia Institute of Technology, Emory University

Good health and well-being

Openalex Percentile: Top 96%

Mechanical Circulatory Support Devices

DOI Open Access PDF OpenAlex

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