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Research on Medical Record Entity Recognition Based onCRF and Bi-LSTM-CRF(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年01期
Page:
81-85
Research Field:
机器学习
Publishing date:

Info

Title:
Research on Medical Record Entity Recognition Based onCRF and Bi-LSTM-CRF
Author(s):
Yang Ronggen1Wang Bo2Gong Lejun2
(1.College of Intelligent Science and Control Engineering,Jinling Institute of Technology,Nanjing 211169,China)(2.Big Data Security and Intelligent Processing Key Laboratory of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)
Keywords:
electronic medical recordnamed entity extractionconditional random fieldfeature templatebidirectional long-term short-term memory network
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2022.01.012
Abstract:
With the rapid increase in the amount of electronic medical record data,how to use electronic medical record resources in depth and efficiency has become more and more important. This article starts from the real medical record,through the manual annotation of 108 medical records of real medical records and three types of feature templates,using conditional random field and the bidirectional long-term short-term memory network conditional random field. Experiments on the extraction of cardiovascular electronic disease named entities and comparative analysis are conducted. The results show that CRF has better extraction performance,and that it is a more suitable method for extracting medical record named entities for small-scale and partially formatted medical record texts.

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Last Update: 2022-03-15