Comparison With Baseline Models

To further investigate the performance of TOP-Net, we designed subexperiments 1, 2, and 3 to obtain a comprehensive assessment. In subexperiment 1, the model was acquired without considering personal information and bidirection memory functions. That is, LSTM and convolutional neural network models were obtained in a total cohort without considering the personal information of patients. The structure of the LSTM was consistent with that of a BiLSTM, and the convolutional neural network model had 2 convolutional layers. In subexperiment 2, conventional machine learning methods, including extreme gradient boosting [49], multilayer perceptron, and random forest, were compared with TOP-Net with default model parameters. In subexperiment 3, different feature combinations were examined: (1) all vital signs, (2) heart rate, (3) heart rate and respiratory rate, and (4) heart rate and SpO₂.