103 related articles for article (PubMed ID: 31259040)
1. 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]
2. 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]
3. Cost-aware active learning for named entity recognition in clinical text.
Wei Q; Chen Y; Salimi M; Denny JC; Mei Q; Lasko TA; Chen Q; Wu S; Franklin A; Cohen T; Xu H
J Am Med Inform Assoc; 2019 Nov; 26(11):1314-1322. PubMed ID: 31294792
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. Active learning reduces annotation time for clinical concept extraction.
Kholghi M; Sitbon L; Zuccon G; Nguyen A
Int J Med Inform; 2017 Oct; 106():25-31. PubMed ID: 28870380
[TBL] [Abstract][Full Text] [Related]
8. Prediction of myopia development among Chinese school-aged children using refraction data from electronic medical records: A retrospective, multicentre machine learning study.
Lin H; Long E; Ding X; Diao H; Chen Z; Liu R; Huang J; Cai J; Xu S; Zhang X; Wang D; Chen K; Yu T; Wu D; Zhao X; Liu Z; Wu X; Jiang Y; Yang X; Cui D; Liu W; Zheng Y; Luo L; Wang H; Chan CC; Morgan IG; He M; Liu Y
PLoS Med; 2018 Nov; 15(11):e1002674. PubMed ID: 30399150
[TBL] [Abstract][Full Text] [Related]
9. Applying active learning to assertion classification of concepts in clinical text.
Chen Y; Mani S; Xu H
J Biomed Inform; 2012 Apr; 45(2):265-72. PubMed ID: 22127105
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.
Du Z; Yang Y; Zheng J; Li Q; Lin D; Li Y; Fan J; Cheng W; Chen XH; Cai Y
JMIR Med Inform; 2020 Jul; 8(7):e17257. PubMed ID: 32628616
[TBL] [Abstract][Full Text] [Related]
13. Clinical Text Data in Machine Learning: Systematic Review.
Spasic I; Nenadic G
JMIR Med Inform; 2020 Mar; 8(3):e17984. PubMed ID: 32229465
[TBL] [Abstract][Full Text] [Related]
14. The effectiveness of internet-based e-learning on clinician behavior and patient outcomes: a systematic review protocol.
Sinclair P; Kable A; Levett-Jones T
JBI Database System Rev Implement Rep; 2015 Jan; 13(1):52-64. PubMed ID: 26447007
[TBL] [Abstract][Full Text] [Related]
15. Active Self-Paced Learning for Cost-Effective and Progressive Face Identification.
Lin L; Wang K; Meng D; Zuo W; Zhang L
IEEE Trans Pattern Anal Mach Intell; 2018 Jan; 40(1):7-19. PubMed ID: 28092522
[TBL] [Abstract][Full Text] [Related]
16. Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.
Lee HJ; Zhang Y; Roberts K; Xu H
AMIA Annu Symp Proc; 2017; 2017():1070-1079. PubMed ID: 29854175
[TBL] [Abstract][Full Text] [Related]
17. Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records.
Afzal Z; Schuemie MJ; van Blijderveen JC; Sen EF; Sturkenboom MC; Kors JA
BMC Med Inform Decis Mak; 2013 Mar; 13():30. PubMed ID: 23452306
[TBL] [Abstract][Full Text] [Related]
18. Predicting sample size required for classification performance.
Figueroa RL; Zeng-Treitler Q; Kandula S; Ngo LH
BMC Med Inform Decis Mak; 2012 Feb; 12():8. PubMed ID: 22336388
[TBL] [Abstract][Full Text] [Related]
19. Semi-supervised Learning for Phenotyping Tasks.
Dligach D; Miller T; Savova GK
AMIA Annu Symp Proc; 2015; 2015():502-11. PubMed ID: 26958183
[TBL] [Abstract][Full Text] [Related]
20. Integrated Random Negative Sampling and Uncertainty Sampling in Active Learning Improve Clinical Drug Safety Drug-Drug Interaction Information Retrieval.
Xie W; Wang L; Cheng Q; Wang X; Wang Y; Bi H; He B; Feng W
Front Pharmacol; 2020; 11():582470. PubMed ID: 34017245
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
