Chinese Clinical Named Entity Recognition with Word-Level Information Incorporating Dictionaries

Electronic Medical Records (EMRs) are the digital equivalent of paper records, which include treatment and medical history about a patient. At present, the main research goal of Chinese EMRS is to accurately recognize the body parts, drugs, illnesses and other information in the Chinese medical process. Implementing EMRs can boost both the quality and safety of patient care. In Chinese EMRs, how to accurately recognize named entities is important because it is useful to predict the disease risk, therapeutic method and recovery probability. This paper proposes a novel deep learning framework, which uses character-word joint embedding and combines different feature information based on the dictionary. Compared with the predecessors, we incorporate word-level information based on the basic Bi-LSTM model. In addition, we propose an improved n-gram feature encoding method and compare it with PDET feature and PIET feature. Our experimental results demonstrate that our proposed model performs the best in predicting named entities in Chinese EMRs.