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

Summary/Abstract: 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. The precision rate as regards diagnosis can be optimized through deep learning algorithms and smart networked medical devices. Deep neural network-driven Internet of Things and wearable devices are pivotal in patient-oriented medical real-time analytics and smart healthcare. Artificial intelligence-powered diagnostic tools and machine learning-based real-time data sensing and processing have been integrated into big healthcare data analytics. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

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