Prediction of individual COVID-19 diagnosis using baseline demographics and lab data

Abstract The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting indivi...

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Autores principales:	Jimmy Zhang, Tomi Jun, Jordi Frank, Sharon Nirenberg, Patricia Kovatch, Kuan-lin Huang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a6d017c9b7b44b3f80c3aba8f956ec04
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spelling	oai:doaj.org-article:a6d017c9b7b44b3f80c3aba8f956ec042021-12-02T15:23:17ZPrediction of individual COVID-19 diagnosis using baseline demographics and lab data10.1038/s41598-021-93126-72045-2322https://doaj.org/article/a6d017c9b7b44b3f80c3aba8f956ec042021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93126-7https://doaj.org/toc/2045-2322Abstract The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.Jimmy ZhangTomi JunJordi FrankSharon NirenbergPatricia KovatchKuan-lin HuangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jimmy Zhang Tomi Jun Jordi Frank Sharon Nirenberg Patricia Kovatch Kuan-lin Huang Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
description	Abstract The global surge in COVID-19 cases underscores the need for fast, scalable, and reliable testing. Current COVID-19 diagnostic tests are limited by turnaround time, limited availability, or occasional false findings. Here, we developed a machine learning-based framework for predicting individual COVID-19 positive diagnosis relying only on readily-available baseline data, including patient demographics, comorbidities, and common lab values. Leveraging a cohort of 31,739 adults within an academic health system, we trained and tested multiple types of machine learning models, achieving an area under the curve of 0.75. Feature importance analyses highlighted serum calcium levels, temperature, age, lymphocyte count, smoking, hemoglobin levels, aspartate aminotransferase levels, and oxygen saturation as key predictors. Additionally, we developed a single decision tree model that provided an operable method for stratifying sub-populations. Overall, this study provides a proof-of-concept that COVID-19 diagnosis prediction models can be developed using only baseline data. The resulting prediction can complement existing tests to enhance screening and pandemic containment workflows.
format	article
author	Jimmy Zhang Tomi Jun Jordi Frank Sharon Nirenberg Patricia Kovatch Kuan-lin Huang
author_facet	Jimmy Zhang Tomi Jun Jordi Frank Sharon Nirenberg Patricia Kovatch Kuan-lin Huang
author_sort	Jimmy Zhang
title	Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
title_short	Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
title_full	Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
title_fullStr	Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
title_full_unstemmed	Prediction of individual COVID-19 diagnosis using baseline demographics and lab data
title_sort	prediction of individual covid-19 diagnosis using baseline demographics and lab data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/a6d017c9b7b44b3f80c3aba8f956ec04
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Prediction of individual COVID-19 diagnosis using baseline demographics and lab data

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