Using artificial neural networks for pattern recognition of post-surgical infections

• Autor(a) principal: Tomé, Fernanda
• Data de Publicação: 2023
• Outros Autores: Michelin, Lessandra, Lins, Rodrigo Schrage, Bringmann, Deise Renata, Corso, Leandro Luis
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Health Review
• Texto Completo: https://ojs.brazilianjournals.com.br/ojs/index.php/BJHR/article/view/57216
• topic: surgical site infections; infection prevention; post-operative care; biomedical engineering; statistics

The objective is to use Artificial Intelligence (AI) for identifying which surgical patients have a likelihood ratio of developing an infection. We included in the study all the patients who underwent surgeries with wound class considered clean at a regional public hospital in Brazil. The first step was a retrospective analysis of risk factors and a correlation test for identifying which clinical variables are best related to post-discharge infections. Then, we developed an Artificial Neural Network (ANN) for pattern recognition to detect incidence of infections.  The ANN can make accurate predictions in 77.3% of the cases in which an infection will occur, and the AUROC of the model is 0.9050. Thus, it is possible to take actions before the patients develop it, improving the quality of life and mental health as well as avoid increasing costs.