Study on Named Entity Recognition of Chinese Electronic Medical Records
Purpose/Significance To explore the technical feasibility of named entity recognition(NER)method based on Chinese electronic medical records(EMR)in the construction of medical knowledge graph and related application promotion.Method/Process The word embedding representation model is refined by using real EMR data,and the proprietary embedding representation of medical terms is constructed.Moreover,multiple models such as convolutional neural network(CNN)are used to extract local semantic features to realize the recognition of Chinese medical named entities based on stacked attention network(SAN).Result/Conclusion The F1 val-ue of SAN model reaches 91.5%,which has stronger performance of medical NER than other models,so as to further solve the difficulty of Chinese medical NER,achieve comprehensive and in-depth extraction of global semantic features,and reduce the time cost.
electronic medical recordsnamed entity recognitionstacked attention network
万方数据
维普
