MONITORING THE SPREAD OF HOSPITAL INFECTIONS USING ARTIFICIAL INTELLIGENCE

Abstract

This thesis analyzes the factors influencing the spread of hospital infections (nosocomial infections) and the application of artificial intelligence (AI) technologies in their prevention. The study examines the possibilities of early detection of infectious risk zones through AI-based monitoring systems, digital tracking of patient and medical staff movement, real-time processing of epidemiological data, and the development of predictive models. Proposals have been developed to improve infection process forecasting, strengthen control in sterilization procedures, and enhance the effectiveness of clinical decision-making using AI technologies. The research results are aimed at improving hospital hygiene quality, reducing the spread of infections, and creating a safe environment in medical institutions.