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Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients
The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations r...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044277/ https://www.ncbi.nlm.nih.gov/pubmed/35494832 http://dx.doi.org/10.7717/peerj-cs.889 |
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author | Nazir, Amril Ampadu, Hyacinth Kwadwo |
author_facet | Nazir, Amril Ampadu, Hyacinth Kwadwo |
author_sort | Nazir, Amril |
collection | PubMed |
description | The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios. |
format | Online Article Text |
id | pubmed-9044277 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90442772022-04-28 Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients Nazir, Amril Ampadu, Hyacinth Kwadwo PeerJ Comput Sci Bioinformatics The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios. PeerJ Inc. 2022-03-17 /pmc/articles/PMC9044277/ /pubmed/35494832 http://dx.doi.org/10.7717/peerj-cs.889 Text en © 2022 Nazir and Ampadu 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Nazir, Amril Ampadu, Hyacinth Kwadwo Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients |
title | Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients |
title_full | Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients |
title_fullStr | Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients |
title_full_unstemmed | Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients |
title_short | Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients |
title_sort | interpretable deep learning for the prediction of icu admission likelihood and mortality of covid-19 patients |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044277/ https://www.ncbi.nlm.nih.gov/pubmed/35494832 http://dx.doi.org/10.7717/peerj-cs.889 |
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