When Patients Recover From COVID-19: Data-Driven Insights From Wearable Technologies.

Autor:	Guo M; Department of Statistics, The George Washington University, Washington, DC, United States., Nguyen L; Department of Computer Science and Data Science, School of Applied Computational Sciences, Meharry Medical College, Nashville, TN, United States., Du H; Department of Statistics, The George Washington University, Washington, DC, United States., Jin F; Department of Statistics, The George Washington University, Washington, DC, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in big data [Front Big Data] 2022 Apr 28; Vol. 5, pp. 801998. Date of Electronic Publication: 2022 Apr 28 (Print Publication: 2022).
DOI:	10.3389/fdata.2022.801998
Abstrakt:	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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Guo, Nguyen, Du and Jin.)
Databáze:	MEDLINE
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