Abstract: The electronic medical record document records the entire process of patient diagnosis and treatment in detail. Medical knowledge is the most abundant in the electronic medical record. Therefore, it is of great value to explore the potential knowledge structure of the electronic medical record document. The primary task for knowledge mining of unstructured electronic medical records is named entity recognition. The existing named entity recognition methods in the medical field face the problems of low quality and insufficient annotation data. At the same time, the existing methods only consider the sequence characteristics of the text, and ignore the dependence between words and characters in the text, which limits the effect of named entity recognition. This paper proposes a medical named entity recognition method based on semisupervised deep learning, which combining the semiautomatic entity annotation method of Chinese encyclopedia with expert authority and the BERT-GCN-CRF framework to perform medical named entity recognition and extraction on electronic medical record text. Taking the real electronic medical record text as the experimental object, the accuracy, recall, and F1 value obtained by this model are significantly improved, and the comprehensive average of P, R and F1 are 84.6%, 84.0% and 84.2%, respectively. At the same time, the workload of manual labeling is significantly reduced. The new method is of great significance to the unstructured text mining of electronic medical records.

Key words: Medical named entity recognition, Electronic medical records, Knowledge mining, Semi-supervised deep learning, BERT-GCN-CRF