Title: An AI knowledge base system for the recognition and personalised treatment of SARS-CoV-2

Authors: Beniamino Di Martino; Gennaro Junior Pezzullo; Alessandro Magliacane; Hung Wei Li; Meng Yen Hsieh

Addresses: Department of Engineering, University of Campania 'Luigi Vanvitelli', Caserta, Italy; Department of Computer Science, University of Vienna, Vienna, Austria; Department of Computer Science and Information Engineering, Asia University, Taiwan, ROC ' Department of Engineering, University of Campania 'Luigi Vanvitelli', Caserta, Italy; Department of Engineering, University of Rome 'Campus Bio-Medico', Rome, Italy ' Department of Engineering, University of Campania 'Luigi Vanvitelli', Caserta, Italy ' Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan, ROC ' Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan, ROC

Abstract: Due to its rapid spread and associated symptoms, conventional methods of prevention and treatment have often proven inadequate to manage SARS-CoV-2, as conventional approaches show limited effectiveness in many scenarios. The ability of the virus to transmit rapidly, even to asymptomatic individuals, and the severity of symptoms in some patients have put significant pressure on healthcare systems. Against this backdrop, the work described aimed to develop an expert system for personalised recognition and treatment of SARS-CoV-2. A clear methodology was defined, supported by conceptual diagrams to outline the logical flow of the problem and proposed solutions. This methodology is based on key components: a Bayesian network to calculate the probability of infection by analysing the patient's symptoms, contacts, and geographic context; and a semantic component to determine the most appropriate treatment using the patient's clinical and personal information, such as allergies or individual risk factors. Once the solution was defined logically, we moved on to formalising the components and designing the workflows, which were implemented using appropriate technologies and open data. Testing of the system carried out through real data simulations, confirmed the system's ability to provide customised patient responses.

Keywords: semantic; ontology; e-health; expert system.

DOI: 10.1504/IJES.2025.149250

International Journal of Embedded Systems, 2025 Vol.18 No.2, pp.125 - 137

Received: 19 Nov 2024
Accepted: 09 Mar 2025
Published online: 20 Oct 2025 *

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