Automated healthcare-associated infection surveillance using an artificial intelligence algorithm

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 neur...

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Detalles Bibliográficos
Autores principales: dos Santos, R.P., Silva, D., Menezes, A., Lukasewicz, S., Dalmora, C.H., Carvalho, O., Giacomazzi, J., Golin, N., Pozza, R., Vaz, T.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Short Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8387762/
https://www.ncbi.nlm.nih.gov/pubmed/34471868
http://dx.doi.org/10.1016/j.infpip.2021.100167
Descripción
Sumario: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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