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1901 related items for PubMed ID: 32736589

  • 1. Prediction of the development of acute kidney injury following cardiac surgery by machine learning.
    Tseng PY, Chen YT, Wang CH, Chiu KM, Peng YS, Hsu SP, Chen KL, Yang CY, Lee OK.
    Crit Care; 2020 Jul 31; 24(1):478. PubMed ID: 32736589
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  • 2. Machine Learning-Based Prediction of Acute Kidney Injury Following Pediatric Cardiac Surgery: Model Development and Validation Study.
    Luo XQ, Kang YX, Duan SB, Yan P, Song GB, Zhang NY, Yang SK, Li JX, Zhang H.
    J Med Internet Res; 2023 Jan 05; 25():e41142. PubMed ID: 36603200
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  • 6. Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.
    Sun R, Li S, Wei Y, Hu L, Xu Q, Zhan G, Yan X, He Y, Wang Y, Li X, Luo A, Zhou Z.
    Int J Surg; 2024 May 01; 110(5):2950-2962. PubMed ID: 38445452
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  • 7. An interpretable machine learning model to predict off-pump coronary artery bypass grafting-associated acute kidney injury.
    Zeng Z, Tian X, Li L, Diao Y, Zhang T.
    Adv Clin Exp Med; 2024 May 01; 33(5):473-481. PubMed ID: 37593773
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  • 10. Preoperative plasma growth-differentiation factor-15 for prediction of acute kidney injury in patients undergoing cardiac surgery.
    Heringlake M, Charitos EI, Erber K, Berggreen AE, Heinze H, Paarmann H.
    Crit Care; 2016 Oct 08; 20(1):317. PubMed ID: 27717384
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  • 12. Application of machine learning model in predicting the likelihood of blood transfusion after hip fracture surgery.
    Chen X, Pan J, Li Y, Tang R.
    Aging Clin Exp Res; 2023 Nov 08; 35(11):2643-2656. PubMed ID: 37733228
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  • 13. Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury.
    Thongprayoon C, Pattharanitima P, Kattah AG, Mao MA, Keddis MT, Dillon JJ, Kaewput W, Tangpanithandee S, Krisanapan P, Qureshi F, Cheungpasitporn W.
    J Clin Med; 2022 Oct 24; 11(21):. PubMed ID: 36362493
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  • 14. Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury.
    Song Y, Zhai W, Ma S, Wu Y, Ren M, Van den Eynde J, Nardi P, Pang PYK, Ali JM, Han J, Guo Z.
    J Thorac Dis; 2024 Jul 30; 16(7):4535-4542. PubMed ID: 39144311
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  • 15. [Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].
    Tang CQ, Li JQ, Xu DY, Liu XB, Hou WJ, Lyu KY, Xiao SC, Xia ZF.
    Zhonghua Shao Shang Za Zhi; 2018 Jun 20; 34(6):343-348. PubMed ID: 29961290
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  • 17. Predictive Accuracy of a Perioperative Laboratory Test-Based Prediction Model for Moderate to Severe Acute Kidney Injury After Cardiac Surgery.
    Demirjian S, Bashour CA, Shaw A, Schold JD, Simon J, Anthony D, Soltesz E, Gadegbeku CA.
    JAMA; 2022 Mar 08; 327(10):956-964. PubMed ID: 35258532
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  • 18. Interpretable machine learning models for early prediction of acute kidney injury after cardiac surgery.
    Jiang J, Liu X, Cheng Z, Liu Q, Xing W.
    BMC Nephrol; 2023 Nov 07; 24(1):326. PubMed ID: 37936067
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  • 20. Tree-based ensemble machine learning models in the prediction of acute respiratory distress syndrome following cardiac surgery: a multicenter cohort study.
    Zhang H, Qian D, Zhang X, Meng P, Huang W, Gu T, Fan Y, Zhang Y, Wang Y, Yu M, Yuan Z, Chen X, Zhao Q, Ruan Z.
    J Transl Med; 2024 Aug 15; 22(1):772. PubMed ID: 39148090
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