Hospital-acquired infections (HAIs) are a significant cause of morbidity and mortality in healthcare settings. Early identification and prevention of HAIs are crucial for improving patient outcomes, reducing healthcare costs, and preventing the spread of infections. In clinical practice, healthcare providers often need to integrate a wide range of patient data, including medical history, laboratory test results, medication usage, surgical procedures, and clinical observations, to assess infection risks and prevent HAIs. As infection control and precision medicine become increasingly important, the challenge remains to predict and prevent infections, especially in patients with subtle or asymptomatic risk factors. Recent advancements in artificial intelligence and data analysis techniques have shown great promise in improving the accuracy and efficiency of infection prediction and prevention. This study aims to develop an AI-assisted decision-making system by integrating multimodal data from electronic health records, lab results, clinical observations, and patient demographics. The objective is to enhance the early identification of patients at risk for HAIs, streamline clinical workflows, and optimize infection control measures. Ultimately, this system seeks to reduce the incidence of hospital-acquired infections, improve patient safety, and enhance overall healthcare quality.
