International Journal of Science, Engineering and TechnologyAuthors: Sujal Sahu, Mukul Sharma, Gulshan, Vansh Verma, Prabhat Mishra
Abstract: AI-based early warning systems represent a transformative approach to improving hospital infection control by enabling the early detection and prediction of hospital-acquired infections (HAIs) among admitted patients. These systems leverage the computational power of artificial intelligence and machine learning models to analyze structured clinical data, including patient demographics, ICU admission status, duration of hospitalization, use of invasive devices such as ventilators and catheters, and underlying comorbidities. By identifying complex patterns and risk factors in real time, the system provides healthcare professionals with timely risk assessments that support proactive clinical decision-making and targeted intervention strategies. This research presents the design, implementation, and evaluation of an AI-based early warning system specifically developed for predicting HAIs within hospital environments. The study utilizes a dataset of patient records and applies machine learning models such as Gradient Boosting, Logistic Regression, and Random Forest to classify infection risk levels. The system is integrated with a web-based interface to facilitate ease of use for healthcare practitioners. Furthermore, the research examines key challenges including data quality, model interpretability, clinical reliability, and ethical considerations related to patient data privacy. The findings highlight the effectiveness of AI-driven approaches in enhancing patient safety, reducing infection rates, and optimizing hospital resource utilization, thereby contributing to more efficient and proactive healthcare management systems.
