Journal Article

A hybrid approach for named entity recognition in Chinese electronic medical record

Liu, Rui, Ji, Bin, Tan, Yusong, Li, Shasha, Wu, Jiaju, Yu, Jie, Wu, Qingbo

2019

Overview

Background With the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic. Methods In this paper, firstly, BiLSTM-CRF model is applied to medical named entity recognition on Chinese electronic medical record. According to the characteristics of Chinese electronic medical records, obtain the low-dimensional word vector of each word in units of sentences. And then input the word vector to BiLSTM to realize automatic extraction of sentence features. And then CRF performs sentence-level word tagging. Secondly, attention mechanism is added between the BiLSTM and the CRF to construct Attention-BiLSTM-CRF model, which can leverage document-level information to alleviate tagging inconsistency. In addition, this paper proposes an entity auto-correct algorithm to rectify entities according to historical entity information. At last, a drug dictionary and post-processing rules are well-built to rectify entities, to further improve performance. Results The final F1 scores of the BiLSTM-CRF and Attention-BiLSTM-CRF model on given test dataset are 90.15 and 90.82% respectively, both of which are higher than 89.26%, which is the best F1 score on the test dataset except ours. Conclusion Our approach can be used to recognize medical named entity on Chinese electronic medical records and achieves the state-of-the-art performance on the given test dataset.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Algorithms / Analysis / Artificial intelligence / Attention / Big data / BiLSTM-CRF / Bioinformatics / China / Chinese electronic medical record / Data management / Data processing / Dictionaries / Drug dictionary / Electronic Health Records / Electronic medical records / Electronic records / Error correction / Feature extraction / Health aspects / Health Informatics / Humans / Information Storage and Retrieval / Information Systems and Communication Service / Language / Management of Computing and Information Systems / Marking / Medical records / Medicine / Medicine & Public Health / Model testing / Named entity recognition / Names / Natural Language Processing / Neural networks / Performance enhancement / Post-production processing / Recognition / Sentences / State of the art / Surgery / Technology / Technology application