Chinese Electronic Medical Record Named Entity Recognition Based on Bi-RNN-LSTM-RNN-CRF

Based on the mainstream deep learning model BiLSTM-CRF, the electronic medical record named entity recognition model Bi-RNN-LSTM-RNN-CRF is established. First collect the electronic medical record data set, then convert the characters into vectors through the word vector tool, enter them into the bidirectional RNN-LSTM-RNN layer for training, and then enter the training results into the CRF layer, calculate the loss function to obtain the prediction results, and record the time that the process took.Finally, repeat the above steps with the traditional BiLSTM-CRF model to compare the results of the two models. Experimental results show that the F1 value of the Bi-RNN-LSTM-RNN-CRF model can reach 97.80%, and the recognition effect is slightly inferior to that of BiLSTM-CRF.