Predicting weaning difficulty for planned extubation patients with an artificial neural network - Hsieh, Meng Hsuen (Medicine (Baltimore), 2019)
This study aims to construct a neural network to predict weaning difficulty among planned extubation patients in intensive care units. This observational cohort study was conducted in eight adult ICUs in a medical center about adult patients experiencing planned extubation. The data of 3602 patients with planned extubation in ICUs of Chi-Mei Medical Center (from Dec. 2009 through Dec. 2011) was used to train and test an artificial neural network (ANN) model. The input features contain 47 clinical risk factors and the outputs are classified into three categories: simple, difficult, and prolonged weaning. A deep ANN model with four hidden layers of 30 neurons each was developed. The accuracy is 0.769 and the area under receiver operating characteristic curve for simple weaning, prolonged weaning, and difficult weaning are 0.910, 0.849, and 0.942 respectively. The results revealed that the ANN model achieved a good performance in prediction the weaning difficulty in planned extubation patients. Such a model will be helpful for predicting ICU patients’ successful planned extubation.