Drug Specification Named Entity Recognition Base on BiLSTM-CRF Model

In order to realize automatic recognition and extraction of entities in unstructured medical texts, a model combining language model conditional random field algorithm (CRF) and Bi-directional Long Short-term Memory networks (BiLSTM) is proposed. We crawled 804 drug specifications for treating asthma from the Internet, and then quantized the normalized field of drug specification word by a vector as the input of the neural network. Compared with the traditional machine learning algorithm CRF model, the system accuracy, recall and F1 value are improved by 6.18%, 5.2% and 4.87%. This model is applicable to extract named entity information from drug specification.