Individualized prediction of COVID-19 adverse outcomes with MLHO

Abstract The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best...

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Autores principales:	Hossein Estiri, Zachary H. Strasser, Shawn N. Murphy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5dd07065d53645818688df403ffe5ebf
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spelling	oai:doaj.org-article:5dd07065d53645818688df403ffe5ebf2021-12-02T13:19:31ZIndividualized prediction of COVID-19 adverse outcomes with MLHO10.1038/s41598-021-84781-x2045-2322https://doaj.org/article/5dd07065d53645818688df403ffe5ebf2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84781-xhttps://doaj.org/toc/2045-2322Abstract The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients’ past medical records (before their COVID-19 infection). MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients’ pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.Hossein EstiriZachary H. StrasserShawn N. MurphyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hossein Estiri Zachary H. Strasser Shawn N. Murphy Individualized prediction of COVID-19 adverse outcomes with MLHO
description	Abstract The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients’ past medical records (before their COVID-19 infection). MLHO’s architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients’ pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.
format	article
author	Hossein Estiri Zachary H. Strasser Shawn N. Murphy
author_facet	Hossein Estiri Zachary H. Strasser Shawn N. Murphy
author_sort	Hossein Estiri
title	Individualized prediction of COVID-19 adverse outcomes with MLHO
title_short	Individualized prediction of COVID-19 adverse outcomes with MLHO
title_full	Individualized prediction of COVID-19 adverse outcomes with MLHO
title_fullStr	Individualized prediction of COVID-19 adverse outcomes with MLHO
title_full_unstemmed	Individualized prediction of COVID-19 adverse outcomes with MLHO
title_sort	individualized prediction of covid-19 adverse outcomes with mlho
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5dd07065d53645818688df403ffe5ebf
work_keys_str_mv	AT hosseinestiri individualizedpredictionofcovid19adverseoutcomeswithmlho AT zacharyhstrasser individualizedpredictionofcovid19adverseoutcomeswithmlho AT shawnnmurphy individualizedpredictionofcovid19adverseoutcomeswithmlho
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Individualized prediction of COVID-19 adverse outcomes with MLHO

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