1256 related articles for article (PubMed ID: 31655273)
1. 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]
2. Automated SNOMED CT concept and attribute relationship detection through a web-based implementation of cTAKES.
Kersloot MG; Lau F; Abu-Hanna A; Arts DL; Cornet R
J Biomed Semantics; 2019 Sep; 10(1):14. PubMed ID: 31533810
[TBL] [Abstract][Full Text] [Related]
3. Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder.
Peng J; Zhao M; Havrilla J; Liu C; Weng C; Guthrie W; Schultz R; Wang K; Zhou Y
BMC Med Inform Decis Mak; 2020 Dec; 20(Suppl 11):322. PubMed ID: 33380331
[TBL] [Abstract][Full Text] [Related]
4. Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes.
Oellrich A; Collier N; Smedley D; Groza T
PLoS One; 2015; 10(1):e0116040. PubMed ID: 25607983
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. A comparative study of current Clinical Natural Language Processing systems on handling abbreviations in discharge summaries.
Wu Y; Denny JC; Rosenbloom ST; Miller RA; Giuse DA; Xu H
AMIA Annu Symp Proc; 2012; 2012():997-1003. PubMed ID: 23304375
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. 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]
9. HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.
Shen F; Peng S; Fan Y; Wen A; Liu S; Wang Y; Wang L; Liu H
J Biomed Inform; 2019 Aug; 96():103246. PubMed ID: 31255713
[TBL] [Abstract][Full Text] [Related]
10. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.
Weng WH; Wagholikar KB; McCray AT; Szolovits P; Chueh HC
BMC Med Inform Decis Mak; 2017 Dec; 17(1):155. PubMed ID: 29191207
[TBL] [Abstract][Full Text] [Related]
11. Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.
Kersloot MG; van Putten FJP; Abu-Hanna A; Cornet R; Arts DL
J Biomed Semantics; 2020 Nov; 11(1):14. PubMed ID: 33198814
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Ensemble Approaches to Recognize Protected Health Information in Radiology Reports.
Horng H; Steinkamp J; Kahn CE; Cook TS
J Digit Imaging; 2022 Dec; 35(6):1694-1698. PubMed ID: 35715655
[TBL] [Abstract][Full Text] [Related]
14. Identify diabetic retinopathy-related clinical concepts and their attributes using transformer-based natural language processing methods.
Yu Z; Yang X; Sweeting GL; Ma Y; Stolte SE; Fang R; Wu Y
BMC Med Inform Decis Mak; 2022 Sep; 22(Suppl 3):255. PubMed ID: 36167551
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification.
Moon S; Carlson LA; Moser ED; Agnikula Kshatriya BS; Smith CY; Rocca WA; Gazzuola Rocca L; Bielinski SJ; Liu H; Larson NB
J Med Internet Res; 2022 Jan; 24(1):e29015. PubMed ID: 35089141
[TBL] [Abstract][Full Text] [Related]
17. Natural language processing to identify lupus nephritis phenotype in electronic health records.
Deng Y; Pacheco JA; Ghosh A; Chung A; Mao C; Smith JC; Zhao J; Wei WQ; Barnado A; Dorn C; Weng C; Liu C; Cordon A; Yu J; Tedla Y; Kho A; Ramsey-Goldman R; Walunas T; Luo Y
BMC Med Inform Decis Mak; 2024 Mar; 22(Suppl 2):348. PubMed ID: 38433189
[TBL] [Abstract][Full Text] [Related]
18. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing.
Han S; Zhang RF; Shi L; Richie R; Liu H; Tseng A; Quan W; Ryan N; Brent D; Tsui FR
J Biomed Inform; 2022 Mar; 127():103984. PubMed ID: 35007754
[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. A Study of Concept Extraction Across Different Types of Clinical Notes.
Kim Y; Riloff E; Hurdle JF
AMIA Annu Symp Proc; 2015; 2015():737-46. PubMed ID: 26958209
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
