Artificial Intelligence Applications in the Diagnosis and Prevention of Hospital-Acquired Infections

Dear Colleagues,

Hospital-acquired infections, also known as healthcare-associated infections (HAI), are nosocomially acquired infections that are typically not present or incubating at the time of admission. These infections are usually acquired after hospitalization and manifest 48 hours after admission to the hospital. These infections mainly include catheter-associated urinary tract infections, central line-associated bloodstream infections, surgical site infections, ventilator-associated pneumonia, hospital-acquired pneumonia, and Clostridium difficile infections. HAIs represent a major global public health problem and are associated with increased morbidity and mortality as well as excess healthcare costs. About 1.7 million HAIs with antibiotic-resistant bacteria (superbugs) occur each year. These bacteria include but are not limited to extended-spectrum beta-lactamase-producing Escherichia coli, methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecium, and multi-drug-resistant (MDR) Acinetobacter baumannii.

Recently, artificial intelligence (AI) and machine learning (ML) applications have been exploited to diagnose, control, and prevent HAIs by supporting the development of HAI surveillance algorithms aimed at understanding the HAI risk factors and improving patient risk stratification and the identification of transmission pathways. Prediction of antimicrobial resistance, outbreaks or infection complications in the hospital setting, automated tracking of hand hygiene compliance, automated laboratory diagnosis, and automated antibiotic prescriptions to prevent misuse or overuse of antibiotics are among the data-analytics-driven uses of AI in the field.

We welcome all submissions related to the above-mentioned topics, which are aimed at the detection, diagnosis, control, and prevention of life-threatening HAIs.

Dr. Dilber Uzun Ozsahin
Dr. Buket Baddal
Dr. Ilker Ozsahin
Guest Editors

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• hospital-acquired infections
• multi-drug resistant (MDR) pathogens
• bacterial infections
• intensive care unit acquired infections
• machine learning
• laboratory diagnosis
• infection prediction
• risk factor prediction
• forecasting
• AI in infection prevention and control