Entity Recognition Approach of Clinical Documents Based on Self-training Framework-Reference-Cited by-同舟云学术

Entity Recognition Approach of Clinical Documents Based on Self-training Framework

Published:2018-08-23 Issue: Volume: Page:259-265
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
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Author:

Che Nannan,Chen Dehua,Le Jiajin

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-8944-2_31

Reference9 articles.

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2. Sebastian Polsterl, Sailesh Conjeti, Nassir Navab. Survival analysis for high-dimensional, heterogeneous medical data: Exploring feature extraction as an alternative to feature selection. Artificial Intelligence in Medicine, 2016, 72:1–11

3. Travis Goodwin, Sanda Harabagiu. KnowledgeGraph and its usage for Retrieving patient Cohorts from Electronic medical Records. International Conference on Semantic computing, 2013:363–370

4. Cheng Ziguang, Zheng Dequan, Li Sheng. Multi-pattern Fusion Based Semi-Supervised Name Entity Recognition [C]. International Conference on Machine Learning and Cybernetics, NJ:IEEE, 2013:45–50

5. Zeng Donghuo, Sun Chengjie, Li Lei, et al. Enlarging Drug Dictionary with Semi- Super vised Learning for Drug Entity Recognition [C]. International Conference on Bioinformatics and Biomedicine, 2016:1929–1931

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