BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

407 related articles for article (PubMed ID: 23489761)

  • 1. 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]  

  • 2. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study.
    Briceño J; Cruz-Ramírez M; Prieto M; Navasa M; Ortiz de Urbina J; Orti R; Gómez-Bravo MÁ; Otero A; Varo E; Tomé S; Clemente G; Bañares R; Bárcena R; Cuervas-Mons V; Solórzano G; Vinaixa C; Rubín A; Colmenero J; Valdivieso A; Ciria R; Hervás-Martínez C; de la Mata M
    J Hepatol; 2014 Nov; 61(5):1020-8. PubMed ID: 24905493
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.
    Ayllón MD; Ciria R; Cruz-Ramírez M; Pérez-Ortiz M; Gómez I; Valente R; O'Grady J; de la Mata M; Hervás-Martínez C; Heaton ND; Briceño J
    Liver Transpl; 2018 Feb; 24(2):192-203. PubMed ID: 28921876
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem.
    Dorado-Moreno M; Pérez-Ortiz M; Gutiérrez PA; Ciria R; Briceño J; Hervás-Martínez C
    Artif Intell Med; 2017 Mar; 77():1-11. PubMed ID: 28545607
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.
    Jiménez F; Sánchez G; Juárez JM
    Artif Intell Med; 2014 Mar; 60(3):197-219. PubMed ID: 24525210
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. Liver transplant recipient selection: MELD vs. clinical judgment.
    Fink MA; Angus PW; Gow PJ; Berry SR; Wang BZ; Muralidharan V; Christophi C; Jones RM
    Liver Transpl; 2005 Jun; 11(6):621-6. PubMed ID: 15915491
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks.
    Fernández Caballero JC; Martínez FJ; Hervás C; Gutiérrez PA
    IEEE Trans Neural Netw; 2010 May; 21(5):750-70. PubMed ID: 20227976
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A clinically based discrete-event simulation of end-stage liver disease and the organ allocation process.
    Shechter SM; Bryce CL; Alagoz O; Kreke JE; Stahl JE; Schaefer AJ; Angus DC; Roberts MS
    Med Decis Making; 2005; 25(2):199-209. PubMed ID: 15800304
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Survival on waiting list for liver transplantation before and after introduction of the model for end-stage liver disease score.
    Tenório AL; Macedo FI; Miranda LE; Fernandes JL; da Silva CM; Neto OL; Lacerda CM
    Transplant Proc; 2010 Mar; 42(2):407-11. PubMed ID: 20304152
    [TBL] [Abstract][Full Text] [Related]  

  • 13. UNOS Liver Registry: ten year survivals.
    Waki K
    Clin Transpl; 2006; ():29-39. PubMed ID: 18368704
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease.
    Cucchetti A; Vivarelli M; Heaton ND; Phillips S; Piscaglia F; Bolondi L; La Barba G; Foxton MR; Rela M; O'Grady J; Pinna AD
    Gut; 2007 Feb; 56(2):253-8. PubMed ID: 16809421
    [TBL] [Abstract][Full Text] [Related]  

  • 15. ABO blood group-related waiting list disparities in liver transplant candidates: effect of the MELD adoption.
    Barone M; Avolio AW; Di Leo A; Burra P; Francavilla A
    Transplantation; 2008 Mar; 85(6):844-9. PubMed ID: 18360266
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Liver transplantation in the United States: a report from the Organ Procurement and Transplantation Network.
    Smith CM; Davies DB; McBride MA
    Clin Transpl; 2000; ():19-30. PubMed ID: 11512312
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Prediction of different types of liver diseases using rule based classification model.
    Kumar Y; Sahoo G
    Technol Health Care; 2013; 21(5):417-32. PubMed ID: 23963359
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Allocation of liver grafts worldwide - Is there a best system?
    Tschuor C; Ferrarese A; Kuemmerli C; Dutkowski P; Burra P; Clavien PA;
    J Hepatol; 2019 Oct; 71(4):707-718. PubMed ID: 31199941
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Prognostic value of MELD score and donor quality in liver transplantation: implications for the donor recipient match.
    Avolio AW; Agnes S; Gasbarrini A; Nure E; Siciliano M; Castagneto M
    Transplant Proc; 2006 May; 38(4):1059-62. PubMed ID: 16757263
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Model for end-stage liver disease score-based allocation of donors for liver transplantation: a spanish multicenter experience.
    de la Mata M; Cuende N; Huet J; Bernardos A; Ferrón JA; Santoyo J; Pascasio JM; Rodrigo J; Solórzano G; Martín-Vivaldi R; Alonso M
    Transplantation; 2006 Dec; 82(11):1429-35. PubMed ID: 17164713
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 21.