500 related articles for article (PubMed ID: 31390003)
1. Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.
Christopoulou F; Tran TT; Sahu SK; Miwa M; Ananiadou S
J Am Med Inform Assoc; 2020 Jan; 27(1):39-46. PubMed ID: 31390003
[TBL] [Abstract][Full Text] [Related]
2. A study of deep learning approaches for medication and adverse drug event extraction from clinical text.
Wei Q; Ji Z; Li Z; Du J; Wang J; Xu J; Xiang Y; Tiryaki F; Wu S; Zhang Y; Tao C; Xu H
J Am Med Inform Assoc; 2020 Jan; 27(1):13-21. PubMed ID: 31135882
[TBL] [Abstract][Full Text] [Related]
3. Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting.
Yang X; Bian J; Fang R; Bjarnadottir RI; Hogan WR; Wu Y
J Am Med Inform Assoc; 2020 Jan; 27(1):65-72. PubMed ID: 31504605
[TBL] [Abstract][Full Text] [Related]
4. Ensemble method-based extraction of medication and related information from clinical texts.
Kim Y; Meystre SM
J Am Med Inform Assoc; 2020 Jan; 27(1):31-38. PubMed ID: 31282932
[TBL] [Abstract][Full Text] [Related]
5. Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings.
Dai HJ; Su CH; Wu CS
J Am Med Inform Assoc; 2020 Jan; 27(1):47-55. PubMed ID: 31334805
[TBL] [Abstract][Full Text] [Related]
6. An ensemble of neural models for nested adverse drug events and medication extraction with subwords.
Ju M; Nguyen NTH; Miwa M; Ananiadou S
J Am Med Inform Assoc; 2020 Jan; 27(1):22-30. PubMed ID: 31197355
[TBL] [Abstract][Full Text] [Related]
7. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records.
Henry S; Buchan K; Filannino M; Stubbs A; Uzuner O
J Am Med Inform Assoc; 2020 Jan; 27(1):3-12. PubMed ID: 31584655
[TBL] [Abstract][Full Text] [Related]
8. Extracting medications and associated adverse drug events using a natural language processing system combining knowledge base and deep learning.
Chen L; Gu Y; Ji X; Sun Z; Li H; Gao Y; Huang Y
J Am Med Inform Assoc; 2020 Jan; 27(1):56-64. PubMed ID: 31591641
[TBL] [Abstract][Full Text] [Related]
9. Adverse Drug Events Detection in Clinical Notes by Jointly Modeling Entities and Relations Using Neural Networks.
Dandala B; Joopudi V; Devarakonda M
Drug Saf; 2019 Jan; 42(1):135-146. PubMed ID: 30649738
[TBL] [Abstract][Full Text] [Related]
10. Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text.
Li Z; Yang Z; Shen C; Xu J; Zhang Y; Xu H
BMC Med Inform Decis Mak; 2019 Jan; 19(Suppl 1):22. PubMed ID: 30700301
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Extraction of Information Related to Drug Safety Surveillance From Electronic Health Record Notes: Joint Modeling of Entities and Relations Using Knowledge-Aware Neural Attentive Models.
Dandala B; Joopudi V; Tsou CH; Liang JJ; Suryanarayanan P
JMIR Med Inform; 2020 Jul; 8(7):e18417. PubMed ID: 32459650
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning.
Li F; Liu W; Yu H
JMIR Med Inform; 2018 Nov; 6(4):e12159. PubMed ID: 30478023
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System.
Kim Y; Heider PM; Lally IR; Meystre SM
JMIR Med Inform; 2021 Apr; 9(4):e22797. PubMed ID: 33885370
[TBL] [Abstract][Full Text] [Related]
17. Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.
Wunnava S; Qin X; Kakar T; Sen C; Rundensteiner EA; Kong X
Drug Saf; 2019 Jan; 42(1):113-122. PubMed ID: 30649736
[TBL] [Abstract][Full Text] [Related]
18. Extracting entities with attributes in clinical text via joint deep learning.
Shi X; Yi Y; Xiong Y; Tang B; Chen Q; Wang X; Ji Z; Zhang Y; Xu H
J Am Med Inform Assoc; 2019 Dec; 26(12):1584-1591. PubMed ID: 31550346
[TBL] [Abstract][Full Text] [Related]
19. Overview of the First Natural Language Processing Challenge for Extracting Medication, Indication, and Adverse Drug Events from Electronic Health Record Notes (MADE 1.0).
Jagannatha A; Liu F; Liu W; Yu H
Drug Saf; 2019 Jan; 42(1):99-111. PubMed ID: 30649735
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
