272 related articles for article (PubMed ID: 23851443)
1. Applying active learning to high-throughput phenotyping algorithms for electronic health records data.
Chen Y; Carroll RJ; Hinz ER; Shah A; Eyler AE; Denny JC; Xu H
J Am Med Inform Assoc; 2013 Dec; 20(e2):e253-9. PubMed ID: 23851443
[TBL] [Abstract][Full Text] [Related]
2. Automated feature selection of predictors in electronic medical records data.
Gronsbell J; Minnier J; Yu S; Liao K; Cai T
Biometrics; 2019 Mar; 75(1):268-277. PubMed ID: 30353541
[TBL] [Abstract][Full Text] [Related]
3. Cost-sensitive Active Learning for Phenotyping of Electronic Health Records.
Ji Z; Wei Q; Franklin A; Cohen T; Xu H
AMIA Jt Summits Transl Sci Proc; 2019; 2019():829-838. PubMed ID: 31259040
[TBL] [Abstract][Full Text] [Related]
4. Applying active learning to supervised word sense disambiguation in MEDLINE.
Chen Y; Cao H; Mei Q; Zheng K; Xu H
J Am Med Inform Assoc; 2013; 20(5):1001-6. PubMed ID: 23364851
[TBL] [Abstract][Full Text] [Related]
5. Surrogate-assisted feature extraction for high-throughput phenotyping.
Yu S; Chakrabortty A; Liao KP; Cai T; Ananthakrishnan AN; Gainer VS; Churchill SE; Szolovits P; Murphy SN; Kohane IS; Cai T
J Am Med Inform Assoc; 2017 Apr; 24(e1):e143-e149. PubMed ID: 27632993
[TBL] [Abstract][Full Text] [Related]
6. Feature extraction for phenotyping from semantic and knowledge resources.
Ning W; Chan S; Beam A; Yu M; Geva A; Liao K; Mullen M; Mandl KD; Kohane I; Cai T; Yu S
J Biomed Inform; 2019 Mar; 91():103122. PubMed ID: 30738949
[TBL] [Abstract][Full Text] [Related]
7. Word2Vec inversion and traditional text classifiers for phenotyping lupus.
Turner CA; Jacobs AD; Marques CK; Oates JC; Kamen DL; Anderson PE; Obeid JS
BMC Med Inform Decis Mak; 2017 Aug; 17(1):126. PubMed ID: 28830409
[TBL] [Abstract][Full Text] [Related]
8. Weakly Semi-supervised phenotyping using Electronic Health records.
Nogues IE; Wen J; Lin Y; Liu M; Tedeschi SK; Geva A; Cai T; Hong C
J Biomed Inform; 2022 Oct; 134():104175. PubMed ID: 36064111
[TBL] [Abstract][Full Text] [Related]
9. Development of an automated phenotyping algorithm for hepatorenal syndrome.
Koola JD; Davis SE; Al-Nimri O; Parr SK; Fabbri D; Malin BA; Ho SB; Matheny ME
J Biomed Inform; 2018 Apr; 80():87-95. PubMed ID: 29530803
[TBL] [Abstract][Full Text] [Related]
10. Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources.
Yu S; Liao KP; Shaw SY; Gainer VS; Churchill SE; Szolovits P; Murphy SN; Kohane IS; Cai T
J Am Med Inform Assoc; 2015 Sep; 22(5):993-1000. PubMed ID: 25929596
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.
Zhao Y; Fu S; Bielinski SJ; Decker PA; Chamberlain AM; Roger VL; Liu H; Larson NB
J Med Internet Res; 2021 Mar; 23(3):e22951. PubMed ID: 33683212
[TBL] [Abstract][Full Text] [Related]
13. Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals.
Teixeira PL; Wei WQ; Cronin RM; Mo H; VanHouten JP; Carroll RJ; LaRose E; Bastarache LA; Rosenbloom ST; Edwards TL; Roden DM; Lasko TA; Dart RA; Nikolai AM; Peissig PL; Denny JC
J Am Med Inform Assoc; 2017 Jan; 24(1):162-171. PubMed ID: 27497800
[TBL] [Abstract][Full Text] [Related]
14. A study of active learning methods for named entity recognition in clinical text.
Chen Y; Lasko TA; Mei Q; Denny JC; Xu H
J Biomed Inform; 2015 Dec; 58():11-18. PubMed ID: 26385377
[TBL] [Abstract][Full Text] [Related]
15. Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.
Nissim N; Shahar Y; Elovici Y; Hripcsak G; Moskovitch R
Artif Intell Med; 2017 Sep; 81():12-32. PubMed ID: 28456512
[TBL] [Abstract][Full Text] [Related]
16. Enabling phenotypic big data with PheNorm.
Yu S; Ma Y; Gronsbell J; Cai T; Ananthakrishnan AN; Gainer VS; Churchill SE; Szolovits P; Murphy SN; Kohane IS; Liao KP; Cai T
J Am Med Inform Assoc; 2018 Jan; 25(1):54-60. PubMed ID: 29126253
[TBL] [Abstract][Full Text] [Related]
17. Relational machine learning for electronic health record-driven phenotyping.
Peissig PL; Santos Costa V; Caldwell MD; Rottscheit C; Berg RL; Mendonca EA; Page D
J Biomed Inform; 2014 Dec; 52():260-70. PubMed ID: 25048351
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.
Zhao SS; Hong C; Cai T; Xu C; Huang J; Ermann J; Goodson NJ; Solomon DH; Cai T; Liao KP
Rheumatology (Oxford); 2020 May; 59(5):1059-1065. PubMed ID: 31535693
[TBL] [Abstract][Full Text] [Related]
20. Evaluating the Portability of Rheumatoid Arthritis Phenotyping Algorithms: A Case Study on French EHRs.
Fabacher T; Sauleau EA; Leclerc Du Sablon N; Bergier H; Gottenberg JE; Coulet A; Névéol A
Stud Health Technol Inform; 2023 May; 302():768-772. PubMed ID: 37203492
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]