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The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity
The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208037/ https://www.ncbi.nlm.nih.gov/pubmed/34129653 http://dx.doi.org/10.1371/journal.pone.0253329 |
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author | Luo, Jiaqing Zhou, Lingyun Feng, Yunyu Li, Bo Guo, Shujin |
author_facet | Luo, Jiaqing Zhou, Lingyun Feng, Yunyu Li, Bo Guo, Shujin |
author_sort | Luo, Jiaqing |
collection | PubMed |
description | The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators. |
format | Online Article Text |
id | pubmed-8208037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82080372021-06-29 The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity Luo, Jiaqing Zhou, Lingyun Feng, Yunyu Li, Bo Guo, Shujin PLoS One Research Article The global pandemic of COVID-19 poses a huge threat to the health and lives of people all over the world, and brings unprecedented pressure to the medical system. We need to establish a practical method to improve the efficiency of treatment and optimize the allocation of medical resources. Due to the influx of a large number of patients into the hospital and the running of medical resources, blood routine test became the only possible check while COVID-19 patients first go to a fever clinic in a community hospital. This study aims to establish an efficient method to identify key indicators from initial blood routine test results for COVID-19 severity prediction. We determined that age is a key indicator for severity predicting of COVID-19, with an accuracy of 0.77 and an AUC of 0.92. In order to improve the accuracy of prediction, we proposed a Multi Criteria Decision Making (MCDM) algorithm, which combines the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Naïve Bayes (NB) classifier, to further select effective indicators from patients’ initial blood test results. The MCDM algorithm selected 3 dominant feature subsets: {Age, WBC, LYMC, NEUT} with a selection rate of 44%, {Age, NEUT, LYMC} with a selection rate of 38%, and {Age, WBC, LYMC} with a selection rate of 9%. Using these feature subsets, the optimized prediction model could achieve an accuracy of 0.82 and an AUC of 0.93. These results indicated that Age, WBC, LYMC, NEUT were the key factors for COVID-19 severity prediction. Using age and the indicators selected by the MCDM algorithm from initial blood routine test results can effectively predict the severity of COVID-19. Our research could not only help medical workers identify patients with severe COVID-19 at an early stage, but also help doctors understand the pathogenesis of COVID-19 through key indicators. Public Library of Science 2021-06-15 /pmc/articles/PMC8208037/ /pubmed/34129653 http://dx.doi.org/10.1371/journal.pone.0253329 Text en © 2021 Luo et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Luo, Jiaqing Zhou, Lingyun Feng, Yunyu Li, Bo Guo, Shujin The selection of indicators from initial blood routine test results to improve the accuracy of early prediction of COVID-19 severity |
title | The selection of indicators from initial blood routine test results
to improve the accuracy of early prediction of COVID-19 severity |
title_full | The selection of indicators from initial blood routine test results
to improve the accuracy of early prediction of COVID-19 severity |
title_fullStr | The selection of indicators from initial blood routine test results
to improve the accuracy of early prediction of COVID-19 severity |
title_full_unstemmed | The selection of indicators from initial blood routine test results
to improve the accuracy of early prediction of COVID-19 severity |
title_short | The selection of indicators from initial blood routine test results
to improve the accuracy of early prediction of COVID-19 severity |
title_sort | selection of indicators from initial blood routine test results
to improve the accuracy of early prediction of covid-19 severity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208037/ https://www.ncbi.nlm.nih.gov/pubmed/34129653 http://dx.doi.org/10.1371/journal.pone.0253329 |
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