641 related articles for article (PubMed ID: 19497782)
1. Predicting the graft survival for heart-lung transplantation patients: an integrated data mining methodology.
Oztekin A; Delen D; Kong ZJ
Int J Med Inform; 2009 Dec; 78(12):e84-96. PubMed ID: 19497782
[TBL] [Abstract][Full Text] [Related]
2. A machine learning-based approach to prognostic analysis of thoracic transplantations.
Delen D; Oztekin A; Kong ZJ
Artif Intell Med; 2010 May; 49(1):33-42. PubMed ID: 20153956
[TBL] [Abstract][Full Text] [Related]
3. Predicting breast cancer survivability: a comparison of three data mining methods.
Delen D; Walker G; Kadam A
Artif Intell Med; 2005 Jun; 34(2):113-27. PubMed ID: 15894176
[TBL] [Abstract][Full Text] [Related]
4. Prediction of graft survival of living-donor kidney transplantation: nomograms or artificial neural networks?
Akl A; Ismail AM; Ghoneim M
Transplantation; 2008 Nov; 86(10):1401-6. PubMed ID: 19034010
[TBL] [Abstract][Full Text] [Related]
5. Predicting three-year kidney graft survival in recipients with systemic lupus erythematosus.
Tang H; Poynton MR; Hurdle JF; Baird BC; Koford JK; Goldfarb-Rumyantzev AS
ASAIO J; 2011; 57(4):300-9. PubMed ID: 21701272
[TBL] [Abstract][Full Text] [Related]
6. Predicting survival causes after out of hospital cardiac arrest using data mining method.
Le Duff F; Muntean C; Cuggia M; Mabo P
Stud Health Technol Inform; 2004; 107(Pt 2):1256-9. PubMed ID: 15361015
[TBL] [Abstract][Full Text] [Related]
7. Predicting kidney transplant survival using tree-based modeling.
Krikov S; Khan A; Baird BC; Barenbaum LL; Leviatov A; Koford JK; Goldfarb-Rumyantzev AS
ASAIO J; 2007; 53(5):592-600. PubMed ID: 17885333
[TBL] [Abstract][Full Text] [Related]
8. Three-year survival rates for all consecutive heart-only and lung-only transplants performed in Eurotransplant, 1997-1999.
Smits JM; Vanhaecke J; Haverich A; de Vries E; Smith M; Rutgrink E; Ramsoebhag A; Hop A; Persijn G; Laufer G
Clin Transpl; 2003; ():89-100. PubMed ID: 15387100
[TBL] [Abstract][Full Text] [Related]
9. [Meta-analysis of the Italian studies on short-term effects of air pollution].
Biggeri A; Bellini P; Terracini B;
Epidemiol Prev; 2001; 25(2 Suppl):1-71. PubMed ID: 11515188
[TBL] [Abstract][Full Text] [Related]
10. A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients.
Selker HP; Griffith JL; Patil S; Long WJ; D'Agostino RB
J Investig Med; 1995 Oct; 43(5):468-76. PubMed ID: 8528758
[TBL] [Abstract][Full Text] [Related]
11. Artificial neural networks and logistic regression as tools for prediction of survival in patients with Stages I and II non-small cell lung cancer.
Marchevsky AM; Patel S; Wiley KJ; Stephenson MA; Gondo M; Brown RW; Yi ES; Benedict WF; Anton RC; Cagle PT
Mod Pathol; 1998 Jul; 11(7):618-25. PubMed ID: 9688182
[TBL] [Abstract][Full Text] [Related]
12. Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation.
Ebell MH
J Fam Pract; 1993 Mar; 36(3):297-303. PubMed ID: 8454976
[TBL] [Abstract][Full Text] [Related]
13. Rating organ failure via adverse events using data mining in the intensive care unit.
Silva A; Cortez P; Santos MF; Gomes L; Neves J
Artif Intell Med; 2008 Jul; 43(3):179-93. PubMed ID: 18486459
[TBL] [Abstract][Full Text] [Related]
14. Reappraisal of HLA antibody analysis and crossmatching in kidney transplantation.
Lee PC; Ozawa M
Clin Transpl; 2007; ():219-26. PubMed ID: 18642453
[TBL] [Abstract][Full Text] [Related]
15. Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data.
Kronek LP; Reddy A
Bioinformatics; 2008 Aug; 24(16):i248-53. PubMed ID: 18689833
[TBL] [Abstract][Full Text] [Related]
16. Investigation of expert rule bases, logistic regression, and non-linear machine learning techniques for predicting response to antiretroviral treatment.
Prosperi MC; Altmann A; Rosen-Zvi M; Aharoni E; Borgulya G; Bazso F; Sönnerborg A; Schülter E; Struck D; Ulivi G; Vandamme AM; Vercauteren J; Zazzi M;
Antivir Ther; 2009; 14(3):433-42. PubMed ID: 19474477
[TBL] [Abstract][Full Text] [Related]
17. An empirical comparison of multivariable methods for estimating risk of death from coronary heart disease.
Knuiman MW; Vu HT; Segal MR
J Cardiovasc Risk; 1997 Apr; 4(2):127-34. PubMed ID: 9304494
[TBL] [Abstract][Full Text] [Related]
18. Nomogram that predicts graft survival probability following living-donor kidney transplant.
Akl A; Mostafa A; Ghoneim MA
Exp Clin Transplant; 2008 Mar; 6(1):30-6. PubMed ID: 18405242
[TBL] [Abstract][Full Text] [Related]
19. Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks.
Cruz-Ramírez M; Hervás-Martínez C; Fernández JC; Briceño J; de la Mata M
Artif Intell Med; 2013 May; 58(1):37-49. PubMed ID: 23489761
[TBL] [Abstract][Full Text] [Related]
20. Data mining methods for classification of Medium-Chain Acyl-CoA dehydrogenase deficiency (MCADD) using non-derivatized tandem MS neonatal screening data.
Van den Bulcke T; Vanden Broucke P; Van Hoof V; Wouters K; Vanden Broucke S; Smits G; Smits E; Proesmans S; Van Genechten T; Eyskens F
J Biomed Inform; 2011 Apr; 44(2):319-25. PubMed ID: 21167313
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
