309 related articles for article (PubMed ID: 30649735)
1. 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]
2. Detecting Adverse Drug Events with Rapidly Trained Classification Models.
Chapman AB; Peterson KS; Alba PR; DuVall SL; Patterson OV
Drug Saf; 2019 Jan; 42(1):147-156. PubMed ID: 30649737
[TBL] [Abstract][Full Text] [Related]
3. MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes.
Yang X; Bian J; Gong Y; Hogan WR; Wu Y
Drug Saf; 2019 Jan; 42(1):123-133. PubMed ID: 30600484
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Automatic Extraction of Comprehensive Drug Safety Information from Adverse Drug Event Narratives in the Korea Adverse Event Reporting System Using Natural Language Processing Techniques.
Kim S; Kang T; Chung TK; Choi Y; Hong Y; Jung K; Lee H
Drug Saf; 2023 Aug; 46(8):781-795. PubMed ID: 37330415
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Extracting adverse drug events from clinical Notes: A systematic review of approaches used.
Modi S; Kasmiran KA; Mohd Sharef N; Sharum MY
J Biomed Inform; 2024 Mar; 151():104603. PubMed ID: 38331081
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. 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]
13. UArizona at the MADE1.0 NLP Challenge.
Xu D; Yadav V; Bethard S
Proc Mach Learn Res; 2018 May; 90():57-65. PubMed ID: 30467557
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.
Luo Y; Thompson WK; Herr TM; Zeng Z; Berendsen MA; Jonnalagadda SR; Carson MB; Starren J
Drug Saf; 2017 Nov; 40(11):1075-1089. PubMed ID: 28643174
[TBL] [Abstract][Full Text] [Related]
16. Overview of the 2022 n2c2 shared task on contextualized medication event extraction in clinical notes.
Mahajan D; Liang JJ; Tsou CH; Uzuner Ö
J Biomed Inform; 2023 Aug; 144():104432. PubMed ID: 37356640
[TBL] [Abstract][Full Text] [Related]
17. Evaluation of clinical named entity recognition methods for Serbian electronic health records.
Kaplar A; Stošović M; Kaplar A; Brković V; Naumović R; Kovačević A
Int J Med Inform; 2022 Aug; 164():104805. PubMed ID: 35653828
[TBL] [Abstract][Full Text] [Related]
18. Ensembles of natural language processing systems for portable phenotyping solutions.
Liu C; Ta CN; Rogers JR; Li Z; Lee J; Butler AM; Shang N; Kury FSP; Wang L; Shen F; Liu H; Ena L; Friedman C; Weng C
J Biomed Inform; 2019 Dec; 100():103318. PubMed ID: 31655273
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text.
Fan Y; Zhou S; Li Y; Zhang R
J Am Med Inform Assoc; 2021 Mar; 28(3):569-577. PubMed ID: 33150942
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
