Intelligent Treatment Priority Assessment for COVID-19 and Influenza Coinfections Using Fuzzy Logic

The co-occurrence of COVID-19 and influenza poses a significant challenge for healthcare systems worldwide, necessitating prompt and accurate treatment prioritization strategies. This paper presents an intelligent treatment priority assessment framework leveraging fuzzy logic to address the complex decision-making process inherent in managing coinfections. Fuzzy logic provides a flexible and intuitive approach to handling uncertainties and vagueness in medical data, enabling the integration of diverse patient parameters for comprehensive assessment. The proposed framework incorporates a hybrid fuzzy inference system that combines expert knowledge with data-driven insights to evaluate the severity and progression of coinfections. By considering a range of clinical factors such as symptoms, laboratory results, and patient demographics, the system generates prioritized treatment recommendations tailored to individual cases. Moreover, the framework adapts dynamically to evolving conditions by continuously updating its knowledge base through machine learning techniques. Key components of the framework include fuzzy rule-based systems for encoding expert knowledge, fuzzy clustering for identifying patient subgroups, and fuzzy inference engines for decision-making. The intelligent treatment priority assessment framework presented in this study offers a promising solution for effectively managing COVID-19 and influenza coinfections. By harnessing the power of fuzzy logic, healthcare professionals can make informed and timely decisions, thereby enhancing patient care and resource allocation in the face of complex infectious disease scenarios. Future research directions may focus on further refining the framework and extending its applicability to other disease contexts.