BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

133 related articles for article (PubMed ID: 38233027)

  • 1. Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions.
    Hamilton DE; Albright J; Seth M; Painter I; Maynard C; Hira RS; Sukul D; Gurm HS
    Eur Heart J; 2024 Feb; 45(8):601-609. PubMed ID: 38233027
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study.
    Huang C; Murugiah K; Mahajan S; Li SX; Dhruva SS; Haimovich JS; Wang Y; Schulz WL; Testani JM; Wilson FP; Mena CI; Masoudi FA; Rumsfeld JS; Spertus JA; Mortazavi BJ; Krumholz HM
    PLoS Med; 2018 Nov; 15(11):e1002703. PubMed ID: 30481186
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A novel tool for reliable and accurate prediction of renal complications in patients undergoing percutaneous coronary intervention.
    Gurm HS; Seth M; Kooiman J; Share D
    J Am Coll Cardiol; 2013 Jun; 61(22):2242-8. PubMed ID: 23721921
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention.
    Mortazavi BJ; Bucholz EM; Desai NR; Huang C; Curtis JP; Masoudi FA; Shaw RE; Negahban SN; Krumholz HM
    JAMA Netw Open; 2019 Jul; 2(7):e196835. PubMed ID: 31290991
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A random forest based risk model for reliable and accurate prediction of receipt of transfusion in patients undergoing percutaneous coronary intervention.
    Gurm HS; Kooiman J; LaLonde T; Grines C; Share D; Seth M
    PLoS One; 2014; 9(5):e96385. PubMed ID: 24816645
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The comparative safety of abciximab versus eptifibatide in patients on dialysis undergoing percutaneous coronary intervention: Insights from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2).
    Sukul D; Seth M; Schreiber T; Hanzel G; Khandelwal A; Cannon LA; Lalonde TA; Gurm HS
    J Interv Cardiol; 2017 Aug; 30(4):291-300. PubMed ID: 28543770
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Percutaneous Coronary Intervention for Chronic Total Occlusion-The Michigan Experience: Insights From the BMC2 Registry.
    Othman H; Seth M; Zein R; Rosman H; Lalonde T; Yamasaki H; Alaswad K; Menees D; Mehta RH; Gurm H; Daher E;
    JACC Cardiovasc Interv; 2020 Jun; 13(11):1357-1368. PubMed ID: 32417095
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Radial PCI and the obesity paradox: Insights from Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2).
    McDonagh JR; Seth M; LaLonde TA; Khandewal AK; Wohns DH; Dixon SR; Gurm HS
    Catheter Cardiovasc Interv; 2016 Feb; 87(2):211-9. PubMed ID: 26010906
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach.
    Behnoush AH; Shariatnia MM; Khalaji A; Asadi M; Yaghoobi A; Rezaee M; Soleimani H; Sheikhy A; Aein A; Yadangi S; Jenab Y; Masoudkabir F; Mehrani M; Iskander M; Hosseini K
    Eur J Med Res; 2024 Jan; 29(1):76. PubMed ID: 38268045
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
    Zack CJ; Senecal C; Kinar Y; Metzger Y; Bar-Sinai Y; Widmer RJ; Lennon R; Singh M; Bell MR; Lerman A; Gulati R
    JACC Cardiovasc Interv; 2019 Jul; 12(14):1304-1311. PubMed ID: 31255564
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study.
    Ma M; Wan X; Chen Y; Lu Z; Guo D; Kong H; Pan B; Zhang H; Chen D; Xu D; Sun D; Lang H; Zhou C; Li T; Cao C
    J Transl Med; 2023 Jul; 21(1):517. PubMed ID: 37525240
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Machine learning models for prediction of adverse events after percutaneous coronary intervention.
    Niimi N; Shiraishi Y; Sawano M; Ikemura N; Inohara T; Ueda I; Fukuda K; Kohsaka S
    Sci Rep; 2022 Apr; 12(1):6262. PubMed ID: 35428765
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Validation of National Cardiovascular Data Registry risk models for mortality, bleeding and acute kidney injury in interventional cardiology at a German Heart Center.
    Wolff G; Lin Y; Quade J; Bader S; Kosejian L; Brockmeyer M; Karathanos A; Parco C; Krieger T; Heinen Y; Perings S; Albert A; Icks A; Kelm M; Schulze V
    Clin Res Cardiol; 2020 Feb; 109(2):235-245. PubMed ID: 31236693
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine Learning on High-Dimensional Data to Predict Bleeding Post Percutaneous Coronary Intervention.
    Rayfield C; Agasthi P; Mookadam F; Yang EH; Venepally NR; Ramakrishna H; Slomka P; Holmes DR; Arsanjani R
    J Invasive Cardiol; 2020 May; 32(5):E122-E129. PubMed ID: 32357133
    [TBL] [Abstract][Full Text] [Related]  

  • 15. The epidemiology and outcomes of percutaneous coronary intervention before high-risk noncardiac surgery in contemporary practice: insights from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) Registry.
    Muthappan P; Smith D; Aronow HD; Eagle K; Wohns D; Fox J; Share D; Gurm HS
    J Am Heart Assoc; 2014 May; 3(3):e000388. PubMed ID: 24820654
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine-learning Models Predict 30-Day Mortality, Cardiovascular Complications, and Respiratory Complications After Aseptic Revision Total Joint Arthroplasty.
    Abraham VM; Booth G; Geiger P; Balazs GC; Goldman A
    Clin Orthop Relat Res; 2022 Nov; 480(11):2137-2145. PubMed ID: 35767804
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Validated contemporary risk model of acute kidney injury in patients undergoing percutaneous coronary interventions: insights from the National Cardiovascular Data Registry Cath-PCI Registry.
    Tsai TT; Patel UD; Chang TI; Kennedy KF; Masoudi FA; Matheny ME; Kosiborod M; Amin AP; Weintraub WS; Curtis JP; Messenger JC; Rumsfeld JS; Spertus JA
    J Am Heart Assoc; 2014 Dec; 3(6):e001380. PubMed ID: 25516439
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Predicting complications of percutaneous coronary intervention using a novel support vector method.
    Lee G; Gurm HS; Syed Z
    J Am Med Inform Assoc; 2013; 20(4):778-86. PubMed ID: 23599229
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A contemporary simple risk score for prediction of contrast-associated acute kidney injury after percutaneous coronary intervention: derivation and validation from an observational registry.
    Mehran R; Owen R; Chiarito M; Baber U; Sartori S; Cao D; Nicolas J; Pivato CA; Nardin M; Krishnan P; Kini A; Sharma S; Pocock S; Dangas G
    Lancet; 2021 Nov; 398(10315):1974-1983. PubMed ID: 34793743
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Risk of acute kidney injury after percutaneous coronary interventions using radial versus femoral vascular access: insights from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium.
    Kooiman J; Seth M; Dixon S; Wohns D; LaLonde T; Rao SV; Gurm HS
    Circ Cardiovasc Interv; 2014 Apr; 7(2):190-8. PubMed ID: 24569598
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.