Embedded electronic medical record text data mining using neural network association classification algorithm

Abstract

AbstractThe embedded electronic medical record (EMR) information is analysed through a neural network algorithm in the field of deep learning to mine the main sentence of EMR text. The pure convolutional neural network model (PCNN) based on pooled and non‐pooled analysis is constructed based on deep learning CNN to mine EMR text. An association classification algorithm based on neural network is proposed, which integrates classification rules into the neural network and realizes the rapid establishment of neural network structure and parameter setting. Character embedding is imported and combined with a sliding window. Then, CNN is adopted to classify the Chinese character tags of the neural network model. Finally, the model is applied to the word segmentation of the actual pregnant women's EMR text. The results show that the scores of the Biomedical Engineering Society and Bioelectronics in Peking University and Microsoft Reserved Partition datasets are between 0.9516 and 0.9684 and the performance is basically the same, which shows that the model has strong stability. The performance of the max‐pooling, n‐max pooling, and mean‐pooling are all between 0.8004 and 0.8634, but the above results are not as good as the model without pooling layer. For this new fast method to obtain neural network parameters, once the classification rules are given, the parameters in PCNN can be set quickly, and provide a practical basis for the development of deep learning in the medical field.