Automated healthcare-associated infection surveillance using an artificial intelligence algorithm
Rodrigo Pires dos Santos, D. Silva, Arul Menezes, S. Lukasewicz, Camila Hubner Dalmora, Otávio Luiz da Fontoura Carvalho, Juliana Giacomazzi, Nicole Alberti Golin, Roberta Pozza, Tiago Andres Vaz
Abstract
Healthcare-associated infections (HAIs) are among the most common adverse events in hospitals. We used artificial intelligence (AI) algorithms for infection surveillance in a cohort study. The model correctly detected 67 out of 73 patients with HAIs. The final model used a multilayer perceptron neural network achieving an area under receiver operating curve (AUROC) of 90.27%; specificity of 78.86%; sensitivity of 88.57%. Respiratory infections had the best results (AUROC ≥93.47%). The AI algorithm could identify most HAIs. AI is a feasible method for HAI surveillance, has the potential to save time, promote accurate hospital-wide surveillance, and improve infection prevention performance.