Automated healthcare-associated infection surveillance using an artificial intelligence algorithm - R.P. dos Santos; D. Silva; A. Menezes; S. Lukasewicz; C.H. Dalmora; O. Carvalho; J. Giacomazzi; N. Golin; R. Pozza; T.A. Vaz (Elsevier, 2021)
Summary: 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.