Artificial intelligence to predict bed bath time in Intensive Care Units.

ABSTRACT

OBJECTIVES: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. METHODS: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. RESULTS: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. CONCLUSIONS: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.