• Jian Hu, Yang Fei, and Wei-Qin Li.
• School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. fei_yanggood@163.com.
• J Clin Monit Comput. 2022 Jun 1; 36 (3): 839-848.

AbstractTo predict the mortality of acute respiratory distress syndrome (ARDS) by using a radial basis function (RBF) artificial neural network (ANN) model. This study included 217 patients who were admitted between June 2013 and November 2019. The RBF ANN model and logistic regression (LR) model were based on twelve factors related to ARDS. Statistical indexes were used to determine the value of the prediction in the two models. The sensitivity, specificity and accuracy of the RBF ANN model to predict mortality were 83.6%, 88.5% and 82.5%, respectively. Significant differences were found between the RBF ANN and LR models (P < 0.05). When the RBF ANN model was used to identify ARDS, the area under the ROC curve was 0.854 ± 0.029. LDH, organ failure, SP-D and PaO2/FiO2 were the most important independent variables. The RBF ANN model was more likely to predict the mortality of ARDS than the LR model. In addition, it can extract informative risk factors for ARDS.© 2021. The Author(s), under exclusive licence to Springer Nature B.V.

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