Neural network analysis
INFECTION COMPLICATION, POSTOPERATIVE INFECTION
In this study, deep learning is one of the machine learning models that use multilayered neural networks whose hierarchical computational design is partly inspired by a biological neuronal structure and was used to generate the output of the probability of infection complications after surgery [We fitted deep learning models using the training dataset and evaluated prediction performances using the validation set. Hyperparameter (e.g., weights) tuning to identify the optimal values for parameters that were learned during the training process was performed using a back-propagation algorithm iteratively based on the training dataset, referred to as candidate neural network predictive models. Then, we selected the optimal neural network predictive model in terms of the maximum AUC value among those candidate neural networks based on the validation dataset. We selected the optimal threshold with Youden’s index (i.e., sensitivity + specificity—1) based on the validation dataset in terms of a range of candidate thresholds. Based on the optimal threshold, we further calculated the corresponding sensitivity, specificity, NPV, and PPV. Finally, the model comparison was carried out using AUC for classification accuracy of the probability of patients with postoperative infections, in which the higher values, the better accuracy of the predictive model.