Dos Santos RP, Silva D, Menezes A, Lukasewicz S, Dalmora CH, Carvalho O, Giacomazzi J, Golin N, Pozza R, Vaz TA. Automated healthcare-associated infection surveillance using an artificial intelligence algorithm.
Infect Prev Pract 2021;
3:100167. [PMID:
34471868 PMCID:
PMC8387762 DOI:
10.1016/j.infpip.2021.100167]
[Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 07/26/2021] [Indexed: 11/23/2022] Open
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.
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