438 related articles for article (PubMed ID: 31282932)
1. 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]
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. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. 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]
9. 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]
10. Recognition of medication information from discharge summaries using ensembles of classifiers.
Doan S; Collier N; Xu H; Pham HD; Tu MP
BMC Med Inform Decis Mak; 2012 May; 12():36. PubMed ID: 22564405
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. Integrating existing natural language processing tools for medication extraction from discharge summaries.
Doan S; Bastarache L; Klimkowski S; Denny JC; Xu H
J Am Med Inform Assoc; 2010; 17(5):528-31. PubMed ID: 20819857
[TBL] [Abstract][Full Text] [Related]
14. Lancet: a high precision medication event extraction system for clinical text.
Li Z; Liu F; Antieau L; Cao Y; Yu H
J Am Med Inform Assoc; 2010; 17(5):563-7. PubMed ID: 20819865
[TBL] [Abstract][Full Text] [Related]
15. A comparison of word embeddings for the biomedical natural language processing.
Wang Y; Liu S; Afzal N; Rastegar-Mojarad M; Wang L; Shen F; Kingsbury P; Liu H
J Biomed Inform; 2018 Nov; 87():12-20. PubMed ID: 30217670
[TBL] [Abstract][Full Text] [Related]
16. Disease Concept-Embedding Based on the Self-Supervised Method for Medical Information Extraction from Electronic Health Records and Disease Retrieval: Algorithm Development and Validation Study.
Chen YP; Lo YH; Lai F; Huang CH
J Med Internet Res; 2021 Jan; 23(1):e25113. PubMed ID: 33502324
[TBL] [Abstract][Full Text] [Related]
17. Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges.
Wong A; Plasek JM; Montecalvo SP; Zhou L
Pharmacotherapy; 2018 Aug; 38(8):822-841. PubMed ID: 29884988
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.
Kuo TT; Rao P; Maehara C; Doan S; Chaparro JD; Day ME; Farcas C; Ohno-Machado L; Hsu CN
AMIA Annu Symp Proc; 2016; 2016():1880-1889. PubMed ID: 28269947
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]