Emerging Computational Approaches in Telehealth and Telemedicine: A Look at The Post-COVID-19 Landscape

CoviCare: An Integrated System for COVID-19

Author(s): Sagar Yeruva*, Junhua Ding, Ankitraj Gaddam and A Brahmananda Reddy

Pp: 88-115 (28)

DOI: 10.2174/9789815079272122010007

* (Excluding Mailing and Handling)

Abstract

Pandemics are large-scale infectious disease outbreaks that can dramatically increase morbidity and mortality over a wider geographic region and trigger substantial economic, social, and political damage. Currently, the world is facing the coronavirus (COVID-19) pandemic. COVID-19 is considered a dangerous disease affecting all entire humanity and reports death cases in the thousands each day (as per the source from Wikipedia, it is 3,690,000 deaths and 172,000,000 cases identified as COVID-19 positive as of 04-June-2021) and quietly throws dangerous bells on the entire humanity, causing health emergencies in every country, worldwide. Due to the ongoing pandemic, the healthcare infrastructure has been stretched. With the limited healthcare infrastructure and the number of COVID-19 cases spiking up, many countries have opted to treat their patients from the patient home, providing at-home medical facilities and continuous monitoring by medical officials at regular intervals. Health is of considerable importance in the new global situation. Providing smart healthcare is important for all people to monitor continuously and maintain good health. A powerful new mobile application and the usage of machine learning techniques can be an innovative solution to the healthcare problems in these pandemic times for patient management and disease management. This solution can directly impact clinical decision-making. The proposed mobile application is a utility tool for COVID-19 patients during and after the quarantine period/home isolation. This application is aimed at being a friendly interface that can record every detail of COVID-19 patient activity from the day of admission to the day of discharge. This facilitates the proposed system to record all symptoms, medication, responses to medicine, diet aspects, and physical and mental aspects of the patients. The proposed system is designed in such a way that we can get the data from the application that monitors the person’s health activities, and that data will be used for the analysis to extract useful information by using machine learning techniques. The data that is collected from each patient is provided to the machine learning domain to find common features and patterns that help us to gain further insights into the disease and could help to develop better medications, vaccine development, immunisation knowledge base, recovery aspects, and symptomatic approaches for the future generation. This knowledge extracted from the machine learning techniques can be used for better treatment and prediction of disease at the initial stages, which could mitigate the life risk and help to stop the spread of the disease.

© 2024 Bentham Science Publishers | Privacy Policy