Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Muzhe, Nguyen, Long, Du, Hongfei, Jin, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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
_version_ 1784705958145949696
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
work_keys_str_mv AT guomuzhe whenpatientsrecoverfromcovid19datadriveninsightsfromwearabletechnologies
AT nguyenlong whenpatientsrecoverfromcovid19datadriveninsightsfromwearabletechnologies
AT duhongfei whenpatientsrecoverfromcovid19datadriveninsightsfromwearabletechnologies
AT jinfang whenpatientsrecoverfromcovid19datadriveninsightsfromwearabletechnologies