Development and validation of deep learning neural network model
head and neck surgery, postoperative infections
-11, DISEASES, CORONARY ARTERY DISEASE, POSTOPERATIVE INFECTION
The distributions of independent risk factors among training dataset according to the status of postoperative infections are presented in Supplemental Table 5. There were unbalanced distributions of risk factors including gender, ASA, coronary artery disease, head and neck surgery, cardiac surgery, sedation/local anesthesia, laparoscopic surgery, and severity of surgery between patients with and without postoperative infections in the training dataset. In addition, in the validation dataset, there were unbalanced distributions of risk factors including ASA, coronary artery disease, cardiac surgery, and severity of surgery between patients with and without postoperative infections (Supplemental Table 6).All identified independent risk factors in the previous section were divided into two categories: risk factors relevant to baseline variables and risk factors relevant to the surgery. Based on these, deep learning predictive model I included risk factors related to the baseline variables, and deep learning predictive model II included risk factors associated with the baseline variables and those relevant to the surgery. Considering there is no fixed rule for deciding how many hidden layers and neurons should be used in neural network predictive models, we explored the number of neurons, ranging from one to two times of the inputs under a five layers model. We limited the third hidden layer only to include one neutron to facilitate the pooling of information.Of those, there were a total of 132 neural networks using risk factors relevant to baseline variables. The AUC for possible structures of neural network predictive model I is shown in Supplemental Fig. 2. We found the optimal neural network predictive model with the best performance among those 132 possible neural networks with five layers with 6-11-3-1-1, including 1 input layer with 6 nodes of gender, ASA score (II), ASA score (III), ASA (IV), coronary artery disease and other diseases, 3 hidden layers, and 1 output layer (Supplemental Table 7). Supplemental Figure 3 shows the optimal structure of deep learning predictive model I. As shown in Fig. 3, the AUC measure of the ROC curve obtained by deep learning model I was 0.641 (95% CI 0.545–0.737). This predictive model had an overall sensitivity and specificity of 34.2% (95% CI 19.6–51.4) and 88.8% (95% CI 85.6–91.6), respectively, for the prediction of postoperative infections (Table There were 756 neural networks related to all independent risk factors. The AUC for possible structures of neural network predictive model II is shown in Supplemental Fig. 4. We found the optimal neural network predictive model with the best performance among those 765 possible neural networks with five layers with 14-28-24-1-1, including 1 input layer with 14 nodes of gender, ASA score (II), ASA score (III), ASA score (IV), coronary artery disease, other diseases, urology, and kidney surgery, head and neck surgery, cardiac surgery, other surgery, sedation/local anesthesia, laparoscopic surgery, surgical severity (intermediate) and surgical severity (major), 3 hidden layers, and 1 output layer. (Supplemental Table 7). Supplemental Figure 5 shows the optimal structure of deep learning predictive model II. As shown in Fig. 