DL-PER: Deep Learning Model for Chinese Prehospital Emergency Record Classification

Abstract

Prehospital emergency records contain much information about prehospital emergency patients. Extracting important patient information from many records has become the focus of all prehospital emergency personnel. The key to solving this problem is to achieve the automatic classification of prehospital emergency records. This study considers a deep learning-based prehospital emergency record classification model (DL-PER). The model uses a weighted text convolutional neural network to classify prehospital emergency records. First, we use prehospital emergency records to train a bi-directional encoder representation (BERT) model from the transformer. BERT obtains the word vectors. Then, we use a bi-directional long and short-term memory (BiLSTM) model to obtain text features from a global perspective. A weighted text convolutional neural network (WTextCNN) improves this model’s local text feature extraction capability. We used activation functions instead of ReLu activation functions to improve the learning ability of the model. We conducted experiments using prehospital emergency records provided by the Handan Emergency Center. The results showed that the DL-PER model improved the F1 scores by up to 5.7%, 6.8%, 5.7%, and 4.9% on the four data sets, respectively, compared with the BiLSTM model.