An intelligent telemonitoring application for coronavirus patients: reCOVeryaID

Abstract

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.

Enhancing Precision of Telemonitoring of COVID-19 Patients through Expert System Based on IoT Data Elaboration

“…To the best of our knowledge, our proposal represents the first application of artificial intelligence for the aforementioned specific purpose. With respect to classification performance, the achieved results are in line with other AI applications presented in the literature, even if aimed at different tasks in the medical context [42,55]. Furthermore, considering the unweighted confusion matrix (Figure 5a), it is also worth noticing that the majority of misclassifications occurred above the main diagonal, and thus, in a region where their impact was relatively less significant, whereas only in very few cases were there an underestimation of the patient risk of at most one class.…”

“…With respect to the state of the art, the proposed ES complements existing AI systems in the literature that calculate clinical risk or detect abnormal parameters [41,42,[47][48][49][50] since it enables the construction and adaptation of precise and customized datasets specific to individual patients. From these datasets, existing systems can then process their outputs more effectively.…”

“…Additionally, Mohebbi et al [41] presented a fuzzy inference system for categorizing COVID-19 patients based on illness severity, using inputs such as oxygen saturation, lung infection rate, respiratory rate, and the presence of clinical symptoms. Concerning the progression of the disease, a rule-based ES that aims at detecting dangerous trends in the patient's short-and long-term vital signs is presented in [42]. In addition to ESs, machine learning (ML) techniques are applied to support healthcare professionals.…”