A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records-Reference-Cited by-同舟云学术

A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records

Published:2019-04 Issue:S2 Volume:19 Page:
ISSN:1472-6947
Container-title:BMC Medical Informatics and Decision Making
language:en
Short-container-title:BMC Med Inform Decis Mak

Author:

Cai Xiaoling,Dong Shoubin,Hu Jinlong

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s12911-019-0762-7.pdf

Reference23 articles.

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2. Goodwin T, Harabagiu SM. Automatic generation of a qualified medical knowledge graph and its usage for retrieving patient cohorts from electronic medical records. 2013 IEEE seventh international conference on semantic. Computing. 2013;363–70.

3. Rotmensch M, Halpern Y, Tlimat A, Gorng S, Sontag D. Learning a health knowledge graph from electronic medical records. Sci Rep. 2017;7(1):5994.

4. W Zhou, Z Jin, L Liu. A Review of the application of data Mining in the Research of traditional Chinese medicine (in Chinese). Chin J Information Traditional Chin Med. 2014;21(10):131–3.

5. Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H. A comprehensive study of named entity recognition in Chinese clinical text. J Am Med Inform Assoc. 2014;21:808–14.

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