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When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies
Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short tim...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096352/ https://www.ncbi.nlm.nih.gov/pubmed/35574570 http://dx.doi.org/10.3389/fdata.2022.801998 |
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author | Guo, Muzhe Nguyen, Long Du, Hongfei Jin, Fang |
author_facet | Guo, Muzhe Nguyen, Long Du, Hongfei Jin, Fang |
author_sort | Guo, Muzhe |
collection | PubMed |
description | Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short time. This paper proposes a model which combines Long-short Term Memory (LSTM) and Deep Neural Network (DNN) to early and accurately classify disease stages of the patients to address the problem at a low cost. In this model, the LSTM component will exploit temporal features while the DNN component extracts attributed features to enhance the model's classification performance. Our experimental results demonstrate that the proposed model achieves substantially better prediction accuracy than existing state-of-art methods. Moreover, we explore the importance of different vital indicators to help patients and doctors identify the critical factors at different COVID-19 stages. Finally, we create case studies demonstrating the differences between severe and mild patients and show the signs of recovery from COVID-19 disease by extracting shape patterns based on temporal features of patients. In summary, by identifying the disease stages, this research will help patients understand their current disease situation. Furthermore, it will also help doctors to provide patients with an immediate treatment plan remotely that addresses their specific disease stages, thus optimizing their usage of limited medical resources. |
format | Online Article Text |
id | pubmed-9096352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90963522022-05-13 When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies Guo, Muzhe Nguyen, Long Du, Hongfei Jin, Fang Front Big Data Big Data Coronavirus disease 2019 (COVID-19) is known as a contagious disease and caused an overwhelming of hospital resources worldwide. Therefore, deciding on hospitalizing COVID-19 patients or quarantining them at home becomes a crucial solution to manage an extremely big number of patients in a short time. This paper proposes a model which combines Long-short Term Memory (LSTM) and Deep Neural Network (DNN) to early and accurately classify disease stages of the patients to address the problem at a low cost. In this model, the LSTM component will exploit temporal features while the DNN component extracts attributed features to enhance the model's classification performance. Our experimental results demonstrate that the proposed model achieves substantially better prediction accuracy than existing state-of-art methods. Moreover, we explore the importance of different vital indicators to help patients and doctors identify the critical factors at different COVID-19 stages. Finally, we create case studies demonstrating the differences between severe and mild patients and show the signs of recovery from COVID-19 disease by extracting shape patterns based on temporal features of patients. In summary, by identifying the disease stages, this research will help patients understand their current disease situation. Furthermore, it will also help doctors to provide patients with an immediate treatment plan remotely that addresses their specific disease stages, thus optimizing their usage of limited medical resources. Frontiers Media S.A. 2022-04-28 /pmc/articles/PMC9096352/ /pubmed/35574570 http://dx.doi.org/10.3389/fdata.2022.801998 Text en Copyright © 2022 Guo, Nguyen, Du and Jin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Guo, Muzhe Nguyen, Long Du, Hongfei Jin, Fang When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies |
title | When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies |
title_full | When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies |
title_fullStr | When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies |
title_full_unstemmed | When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies |
title_short | When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies |
title_sort | when patients recover from covid-19: data-driven insights from wearable technologies |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9096352/ https://www.ncbi.nlm.nih.gov/pubmed/35574570 http://dx.doi.org/10.3389/fdata.2022.801998 |
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