The medical record system is becoming more and more irreplaceable in the medical industry, and the electronic medical record data continues to grow over time. There is a lot of knowledge and information in these accumulated medical resources that can be used for medical services, but how to obtain this valuable medical information is a difficult problem that needs to be overcome. Event extraction belongs to information extraction technology, which is an effective solution that can automatically mine knowledge and information from text data. Many studies have applied it to the text data of electronic medical records to extract medical events related to medical treatment. They have achieved certain results for medical services. However, these studies usually lack the synergistic consideration of global features and local features of medical text information in terms of Chinese medical record text mining and utilization. To better solve this problem, we try to propose a hybrid neural network model (BCBC) based on CNN-BILSTM-CRF. By integrating CNN and BILSTM, the local and global features of the text are comprehensively extracted, which makes up for the insufficient semantic capture of a single model in the traditional method. Through experimental verification, the hybrid neural network model BCBC proposed in this paper outperforms other previous advanced methods in event extraction and can efficiently complete the event extraction task.

The widely deployed of hospital information systems causes an explosive growth of the electronic medical records (EMRs). It makes the medical structured processing technologies become critical to find researchable data in the large medical dataset. However, the high quality structured processing is a challenging task, in particular due to the inherent complexity and polysemy of medical terminology. In this paper, we propose a novel approach to achieve the joint extraction of events in Chinese electronic medical records, which solves the problem of cascading error transmission in traditional models and the ambiguity of Chinese characters. We first use the Bi-directional Encoder Representation from Transformers(BERT) model to mine features from the preprocessed medical data; then based on the characteristics of Chinese, we use the Bi-directional Long Short-Term Memory(BILSTM) model to capture the semantic information of the context. The experiments were conducted on a real dataset. The F1 score of our model in the identification and classification tasks of event triggers and arguments is the highest, reaching 71.6, 68.1, 55.4 and 46.9, respectively, which proves the effectiveness of the proposed method.