Affiliation:
1. Department of Information Management, Sun Yat-Sen University, Guangzhou 510006, P. R. China
2. School of Artificial Intelligence, Sun Yat-Sen University, Guangzhou 510006, P. R. China
3. Department of Cardiology, General Hospital of Southern Theatre Command of PLA, Guangzhou 510010, P. R. China
Abstract
To improve the recognition ability of clinical named entity recognition (CNER) in a limited number of Chinese electronic medical records, it provides meaningful support for clinical advanced knowledge extraction. In this paper, using CCKS2019 Chinese electronic medical record as an experimental data source, a fusion model enhanced by knowledge graph (KG) is proposed, and the model is applied to specific Chinese CNER tasks. This study consists of three main parts: single-mode model construction and comparison experiment, KG enhancement experiment, and model fusion experiment. The model has achieved good performance in CNER from the results. The accuracy rate, recall rate, and F1 value are 83.825%, 84.705%, and 84.263%, respectively, which is the global optimal, which proves the effectiveness of the model. This provides a good help for further research of medical information.
Funder
Guangzhou Municipal Science and Technology Program key projects
Zhuhai Industry-University-Research Cooperation Project
Publisher
World Scientific Pub Co Pte Ltd
Cited by
1 articles.
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1. Erratum: Medical Named Entity Recognition Model Based on Knowledge Graph Enhancement;International Journal of Pattern Recognition and Artificial Intelligence;2024-08-12