BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

187 related articles for article (PubMed ID: 31437875)

  • 1. Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.
    Nursetyo AA; Syed-Abdul S; Uddin M; Li YJ
    Stud Health Technol Inform; 2019 Aug; 264():10-14. PubMed ID: 31437875
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models.
    Senanayake S; White N; Graves N; Healy H; Baboolal K; Kularatna S
    Int J Med Inform; 2019 Oct; 130():103957. PubMed ID: 31472443
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Using machine learning techniques to develop risk prediction models to predict graft failure following kidney transplantation: protocol for a retrospective cohort study.
    Senanayake S; Barnett A; Graves N; Healy H; Baboolal K; Kularatna S
    F1000Res; 2019; 8():1810. PubMed ID: 32419922
    [No Abstract]   [Full Text] [Related]  

  • 4. Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study.
    Naqvi SAA; Tennankore K; Vinson A; Roy PC; Abidi SSR
    J Med Internet Res; 2021 Aug; 23(8):e26843. PubMed ID: 34448704
    [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. Machine Learning Model to Predict Graft Rejection After Kidney Transplantation.
    Minato ACDS; Hannun PGC; Barbosa AMP; da Rocha NC; Machado-Rugolo J; Cardoso MMA; de Andrade LGM
    Transplant Proc; 2023 Nov; 55(9):2058-2062. PubMed ID: 37730451
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia.
    Mulugeta G; Zewotir T; Tegegne AS; Juhar LH; Muleta MB
    BMC Med Inform Decis Mak; 2023 May; 23(1):98. PubMed ID: 37217892
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation.
    Lau L; Kankanige Y; Rubinstein B; Jones R; Christophi C; Muralidharan V; Bailey J
    Transplantation; 2017 Apr; 101(4):e125-e132. PubMed ID: 27941428
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study.
    Yoo KD; Noh J; Lee H; Kim DK; Lim CS; Kim YH; Lee JP; Kim G; Kim YS
    Sci Rep; 2017 Aug; 7(1):8904. PubMed ID: 28827646
    [TBL] [Abstract][Full Text] [Related]  

  • 10. The future is coming: promising perspectives regarding the use of machine learning in renal transplantation.
    Hannun PGC; Andrade LGM
    J Bras Nefrol; 2019; 41(2):284-287. PubMed ID: 30353909
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Adult-size kidneys without acute tubular necrosis provide exceedingly superior long-term graft outcomes for infants and small children: a single center and UNOS analysis. United Network for Organ Sharing.
    Sarwal MM; Cecka JM; Millan MT; Salvatierra O
    Transplantation; 2000 Dec; 70(12):1728-36. PubMed ID: 11152105
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning.
    Kawakita S; Beaumont JL; Jucaud V; Everly MJ
    Sci Rep; 2020 Oct; 10(1):18409. PubMed ID: 33110142
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning models in predicting graft survival in kidney transplantation: meta-analysis.
    Ravindhran B; Chandak P; Schafer N; Kundalia K; Hwang W; Antoniadis S; Haroon U; Zakri RH
    BJS Open; 2023 Mar; 7(2):. PubMed ID: 36987687
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Role of donor age and acute rejection episodes on long-term graft survival in cadaveric kidney transplantations.
    Emiroğlu R; Yagmurdur MC; Karakayali F; Haberal C; Ozcelik U; Colak T; Haberal M
    Transplant Proc; 2005 Sep; 37(7):2954-6. PubMed ID: 16213272
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning to predict transplant outcomes: helpful or hype? A national cohort study.
    Bae S; Massie AB; Caffo BS; Jackson KR; Segev DL
    Transpl Int; 2020 Nov; 33(11):1472-1480. PubMed ID: 32996170
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Using Artificial Intelligence for Predicting Survival of Individual Grafts in Liver Transplantation: A Systematic Review.
    Wingfield LR; Ceresa C; Thorogood S; Fleuriot J; Knight S
    Liver Transpl; 2020 Jul; 26(7):922-934. PubMed ID: 32274856
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Pretransplant Immune Interplay Between Donor and Recipient Influences Posttransplant Kidney Allograft Function.
    Kamińska D; Kościelska-Kasprzak K; Mazanowska O; Żabińska M; Bartoszek D; Banasik M; Chudoba P; Lepiesza A; Gomułkiewicz A; Dzięgiel P; Krajewska M; Polak W; Klinger M
    Transplant Proc; 2018; 50(6):1658-1661. PubMed ID: 30056877
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Frequency and severity of acute rejection in live- versus cadaveric-donor renal transplants.
    Campbell SB; Hothersall E; Preston J; Brown AM; Hawley CM; Wall D; Griffin AD; Isbel NM; Nicol DL; Johnson DW
    Transplantation; 2003 Nov; 76(10):1452-7. PubMed ID: 14657685
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Predicting long-term outcomes of kidney transplantation in the era of artificial intelligence.
    Badrouchi S; Bacha MM; Ahmed A; Ben Abdallah T; Abderrahim E
    Sci Rep; 2023 Dec; 13(1):21273. PubMed ID: 38042904
    [TBL] [Abstract][Full Text] [Related]  

  • 20. An analysis of 100 primary cadaver kidney transplants.
    Barry JM; Craig DH; Fischer SM; Fuchs EF; Lawson RK; Bennett WM
    J Urol; 1980 Dec; 124(6):783-6. PubMed ID: 7003171
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.