Digital Epidemiological Surveillance, Smart Telemedicine Diagnosis Systems, and Machine Learning-based Real-Time Data Sensing and Processing in COVID-19 Remote Patient Monitoring

Employing recent research results covering digital epidemiological surveillance, smart telemedicine diagnosis systems, and machine learning-based real-time data sensing and processing in COVID-19 remote patient monitoring, and building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. 4.Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Jiang et al., 2020) The efficient deployment and utilization of data fusion (Lăzăroiu and Harrison, 2021) enable accurate evaluation in remote patient monitoring, optimizing preventive care for chronic diseases by use of machine learning-based automated diagnostic systems and artificial intelligence-enabled wearable medical devices.