An intelligent telemonitoring application for coronavirus patients: reCOVeryaID

Frontiers in Big Data

Big Data

Daniela D'Auria¹  Diego Calvanese²  Federica Addabbo³  Raffaele Russo⁴  Alfonso Fedele⁵ 

[1] Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy;Faculty of Engineering, Free University of Bozen-Bolzano, Bolzano, Italy;Department of Computing Science, Umeå University, Umeå, Sweden;Kronosan Srl, Montevergine Hospital, Mercogliano, Italy;Pineta Grande Hospital, Caserta, Italy;University Riuniti Hospital, Ancona, Italy;

关键词: artificial intelligence;    coronavirus;    COVID-19;    eHealth;    long-term monitoring;    rule-based system;    telehealth;    telemedicine;   

DOI  :  10.3389/fdata.2023.1205766

 received in 2023-04-17, accepted in 2023-08-29,  发布年份 2023

来源: Frontiers

【 摘 要 】

The COVID-19 emergency underscored the importance of resolving crucial issues of territorial health monitoring, such as overloaded phone lines, doctors exposed to infection, chronically ill patients unable to access hospitals, etc. In fact, it often happened that people would call doctors/hospitals just out of anxiety, not realizing that they were clogging up communications, thus causing problems for those who needed them most; such people, often elderly, have often felt lonely and abandoned by the health care system because of poor telemedicine. In addition, doctors were unable to follow up on the most serious cases or make sure that others did not worsen. Thus, uring the first pandemic wave we had the idea to design a system that could help people alleviate their fears and be constantly monitored by doctors both in hospitals and at home; consequently, we developed reCOVeryaID, a telemonitoring application for coronavirus patients. It is an autonomous application supported by a knowledge base that can react promptly and inform medical doctors if dangerous trends in the patient's short- and long-term vital signs are detected. In this paper, we also validate the knowledge-base rules in real-world settings by testing them on data from real patients infected with COVID-19.

【 授权许可】

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Copyright © 2023 D'Auria, Russo, Fedele, Addabbo and Calvanese.