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Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN
Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN...
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| 出版年: | PLoS One |
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| 主要な著者: | , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Public Library of Science
2019
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| 主題: | |
| オンライン・アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6497281/ https://ncbi.nlm.nih.gov/pubmed/31048840 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0216046 |
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