677 related articles for article (PubMed ID: 30559093)
1. Clinical Named Entity Recognition From Chinese Electronic Health Records via Machine Learning Methods.
Zhang Y; Wang X; Hou Z; Li J
JMIR Med Inform; 2018 Dec; 6(4):e50. PubMed ID: 30559093
[TBL] [Abstract][Full Text] [Related]
2. Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.
Fang A; Hu J; Zhao W; Feng M; Fu J; Feng S; Lou P; Ren H; Chen X
BMC Med Inform Decis Mak; 2022 Mar; 22(1):72. PubMed ID: 35321705
[TBL] [Abstract][Full Text] [Related]
3. A deep learning model incorporating part of speech and self-matching attention for named entity recognition of Chinese electronic medical records.
Cai X; Dong S; Hu J
BMC Med Inform Decis Mak; 2019 Apr; 19(Suppl 2):65. PubMed ID: 30961622
[TBL] [Abstract][Full Text] [Related]
4. Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations.
Li Y; Wang X; Hui L; Zou L; Li H; Xu L; Liu W
JMIR Med Inform; 2020 Sep; 8(9):e19848. PubMed ID: 32885786
[TBL] [Abstract][Full Text] [Related]
5. Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding-Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model.
Wu H; Ji J; Tian H; Chen Y; Ge W; Zhang H; Yu F; Zou J; Nakamura M; Liao J
JMIR Med Inform; 2021 Dec; 9(12):e26407. PubMed ID: 34855616
[TBL] [Abstract][Full Text] [Related]
6. Chinese Clinical Named Entity Recognition From Electronic Medical Records Based on Multisemantic Features by Using Robustly Optimized Bidirectional Encoder Representation From Transformers Pretraining Approach Whole Word Masking and Convolutional Neural Networks: Model Development and Validation.
Wang W; Li X; Ren H; Gao D; Fang A
JMIR Med Inform; 2023 May; 11():e44597. PubMed ID: 37163343
[TBL] [Abstract][Full Text] [Related]
7. Improving the Named Entity Recognition of Chinese Electronic Medical Records by Combining Domain Dictionary and Rules.
Chen X; Ouyang C; Liu Y; Bu Y
Int J Environ Res Public Health; 2020 Apr; 17(8):. PubMed ID: 32295174
[TBL] [Abstract][Full Text] [Related]
8. De-identifying free text of Japanese electronic health records.
Kajiyama K; Horiguchi H; Okumura T; Morita M; Kano Y
J Biomed Semantics; 2020 Sep; 11(1):11. PubMed ID: 32958039
[TBL] [Abstract][Full Text] [Related]
9. Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network.
Gao Y; Wang Y; Wang P; Gu L
Int J Environ Res Public Health; 2020 Mar; 17(5):. PubMed ID: 32131522
[TBL] [Abstract][Full Text] [Related]
10. Named entity recognition from Chinese adverse drug event reports with lexical feature based BiLSTM-CRF and tri-training.
Chen Y; Zhou C; Li T; Wu H; Zhao X; Ye K; Liao J
J Biomed Inform; 2019 Aug; 96():103252. PubMed ID: 31323311
[TBL] [Abstract][Full Text] [Related]
11. An attention-based deep learning model for clinical named entity recognition of Chinese electronic medical records.
Li L; Zhao J; Hou L; Zhai Y; Shi J; Cui F
BMC Med Inform Decis Mak; 2019 Dec; 19(Suppl 5):235. PubMed ID: 31801540
[TBL] [Abstract][Full Text] [Related]
12. Chinese clinical named entity recognition with radical-level feature and self-attention mechanism.
Yin M; Mou C; Xiong K; Ren J
J Biomed Inform; 2019 Oct; 98():103289. PubMed ID: 31541715
[TBL] [Abstract][Full Text] [Related]
13. Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.
Li S; Deng L; Zhang X; Chen L; Yang T; Qi Y; Jiang T
J Med Internet Res; 2022 Jun; 24(6):e37213. PubMed ID: 35657661
[TBL] [Abstract][Full Text] [Related]
14. Extracting comprehensive clinical information for breast cancer using deep learning methods.
Zhang X; Zhang Y; Zhang Q; Ren Y; Qiu T; Ma J; Sun Q
Int J Med Inform; 2019 Dec; 132():103985. PubMed ID: 31627032
[TBL] [Abstract][Full Text] [Related]
15. Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.
Munkhdalai T; Liu F; Yu H
JMIR Public Health Surveill; 2018 Apr; 4(2):e29. PubMed ID: 29695376
[TBL] [Abstract][Full Text] [Related]
16. Korean clinical entity recognition from diagnosis text using BERT.
Kim YM; Lee TH
BMC Med Inform Decis Mak; 2020 Sep; 20(Suppl 7):242. PubMed ID: 32998724
[TBL] [Abstract][Full Text] [Related]
17. Adversarial training based lattice LSTM for Chinese clinical named entity recognition.
Zhao S; Cai Z; Chen H; Wang Y; Liu F; Liu A
J Biomed Inform; 2019 Nov; 99():103290. PubMed ID: 31557528
[TBL] [Abstract][Full Text] [Related]
18. A comprehensive study of named entity recognition in Chinese clinical text.
Lei J; Tang B; Lu X; Gao K; Jiang M; Xu H
J Am Med Inform Assoc; 2014; 21(5):808-14. PubMed ID: 24347408
[TBL] [Abstract][Full Text] [Related]
19. Chinese clinical named entity recognition with variant neural structures based on BERT methods.
Li X; Zhang H; Zhou XH
J Biomed Inform; 2020 Jul; 107():103422. PubMed ID: 32353595
[TBL] [Abstract][Full Text] [Related]
20. Legal Text Recognition Using LSTM-CRF Deep Learning Model.
Xu H; Hu B
Comput Intell Neurosci; 2022; 2022():9933929. PubMed ID: 35341203
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
