These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

383 related articles for article (PubMed ID: 33726966)

  • 1. Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation.
    He ZL; Zhou JB; Liu ZK; Dong SY; Zhang YT; Shen T; Zheng SS; Xu X
    Hepatobiliary Pancreat Dis Int; 2021 Jun; 20(3):222-231. PubMed ID: 33726966
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation.
    Zhang Y; Yang D; Liu Z; Chen C; Ge M; Li X; Luo T; Wu Z; Shi C; Wang B; Huang X; Zhang X; Zhou S; Hei Z
    J Transl Med; 2021 Jul; 19(1):321. PubMed ID: 34321016
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [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; 34(6):343-348. PubMed ID: 29961290
    [No Abstract]   [Full Text] [Related]  

  • 4. 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; 24(1):478. PubMed ID: 32736589
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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; 24(1):326. PubMed ID: 37936067
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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; 25():e41142. PubMed ID: 36603200
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Application of Machine Learning Algorithms to Predict Acute Kidney Injury in Elderly Orthopedic Postoperative Patients.
    Chen Q; Zhang Y; Zhang M; Li Z; Liu J
    Clin Interv Aging; 2022; 17():317-330. PubMed ID: 35386749
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV.
    Tian J; Cui R; Song H; Zhao Y; Zhou T
    Int Urol Nephrol; 2024 Jan; 56(1):237-247. PubMed ID: 37256426
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: comparison of machine learning methods and conventional regression.
    Zhao X; Lu Y; Li S; Guo F; Xue H; Jiang L; Wang Z; Zhang C; Xie W; Zhu F
    Ren Fail; 2022 Dec; 44(1):1326-1337. PubMed ID: 35930309
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Machine-learning predictions for acute kidney injuries after coronary artery bypass grafting: a real-life muticenter retrospective cohort study.
    Jia T; Xu K; Bai Y; Lv M; Shan L; Li W; Zhang X; Li Z; Wang Z; Zhao X; Li M; Zhang Y
    BMC Med Inform Decis Mak; 2023 Nov; 23(1):270. PubMed ID: 37996844
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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; 110(5):2950-2962. PubMed ID: 38445452
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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; 20(1):317. PubMed ID: 27717384
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Machine learning for early discrimination between transient and persistent acute kidney injury in critically ill patients with sepsis.
    Luo XQ; Yan P; Zhang NY; Luo B; Wang M; Deng YH; Wu T; Wu X; Liu Q; Wang HS; Wang L; Kang YX; Duan SB
    Sci Rep; 2021 Oct; 11(1):20269. PubMed ID: 34642418
    [TBL] [Abstract][Full Text] [Related]  

  • 14. [Comparison of machine learning and Logistic regression model in predicting acute kidney injury after cardiac surgery: data analysis based on MIMIC-III database].
    Xiong W; Zhang L; She K; Xu G; Bai S; Liu X
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2022 Nov; 34(11):1188-1193. PubMed ID: 36567564
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury.
    Churpek MM; Carey KA; Edelson DP; Singh T; Astor BC; Gilbert ER; Winslow C; Shah N; Afshar M; Koyner JL
    JAMA Netw Open; 2020 Aug; 3(8):e2012892. PubMed ID: 32780123
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Prediction of Acute Kidney Injury after Extracorporeal Cardiac Surgery (CSA-AKI) by Machine Learning Algorithms.
    Tong Y; Niu X; Liu F
    Heart Surg Forum; 2023 Oct; 26(5):E537-E551. PubMed ID: 37920093
    [TBL] [Abstract][Full Text] [Related]  

  • 17. [Risk factors analysis of renal replacement therapy after liver transplantation and prognosis effect of initial treatment time].
    Dong Z; Shi L; Ye L; Xu Z; Zhou L
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2018 Nov; 30(11):1056-1060. PubMed ID: 30541645
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Machine learning for the prediction of acute kidney injury in patients with sepsis.
    Yue S; Li S; Huang X; Liu J; Hou X; Zhao Y; Niu D; Wang Y; Tan W; Wu J
    J Transl Med; 2022 May; 20(1):215. PubMed ID: 35562803
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Early acute kidney injury after liver transplantation in patients with normal preoperative renal function.
    Tan L; Yang Y; Ma G; Zhu T; Yang J; Liu H; Zhang W
    Clin Res Hepatol Gastroenterol; 2019 Aug; 43(4):475-482. PubMed ID: 31126850
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma.
    Lee Y; Ryu J; Kang MW; Seo KH; Kim J; Suh J; Kim YC; Kim DK; Oh KH; Joo KW; Kim YS; Jeong CW; Lee SC; Kwak C; Kim S; Han SS
    Sci Rep; 2021 Aug; 11(1):15704. PubMed ID: 34344909
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 20.