BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

287 related articles for article (PubMed ID: 35118099)

  • 1. A Simple Weaning Model Based on Interpretable Machine Learning Algorithm for Patients With Sepsis: A Research of MIMIC-IV and eICU Databases.
    Liu W; Tao G; Zhang Y; Xiao W; Zhang J; Liu Y; Lu Z; Hua T; Yang M
    Front Med (Lausanne); 2021; 8():814566. PubMed ID: 35118099
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: A retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases.
    Peng S; Huang J; Liu X; Deng J; Sun C; Tang J; Chen H; Cao W; Wang W; Duan X; Luo X; Peng S
    Front Cardiovasc Med; 2022; 9():994359. PubMed ID: 36312291
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation.
    Zhou S; Lu Z; Liu Y; Wang M; Zhou W; Cui X; Zhang J; Xiao W; Hua T; Zhu H; Yang M
    Eur J Med Res; 2024 Jan; 29(1):14. PubMed ID: 38172962
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit.
    Zhang Y; Hu J; Hua T; Zhang J; Zhang Z; Yang M
    Sci Rep; 2023 Aug; 13(1):12697. PubMed ID: 37542106
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Development and Validation of Machine Learning Models for Real-Time Mortality Prediction in Critically Ill Patients With Sepsis-Associated Acute Kidney Injury.
    Luo XQ; Yan P; Duan SB; Kang YX; Deng YH; Liu Q; Wu T; Wu X
    Front Med (Lausanne); 2022; 9():853102. PubMed ID: 35783603
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit.
    Zhuang J; Huang H; Jiang S; Liang J; Liu Y; Yu X
    BMC Med Inform Decis Mak; 2023 Sep; 23(1):185. PubMed ID: 37715194
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Development and validation of a novel blending machine learning model for hospital mortality prediction in ICU patients with Sepsis.
    Zeng Z; Yao S; Zheng J; Gong X
    BioData Min; 2021 Aug; 14(1):40. PubMed ID: 34399809
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.
    Huang B; Liang D; Zou R; Yu X; Dan G; Huang H; Liu H; Liu Y
    Ann Transl Med; 2021 May; 9(9):794. PubMed ID: 34268407
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database.
    Lu Z; Zhang J; Hong J; Wu J; Liu Y; Xiao W; Hua T; Yang M
    Front Med (Lausanne); 2021; 8():661710. PubMed ID: 33889591
    [No Abstract]   [Full Text] [Related]  

  • 10. A nomogram for predicting hospital mortality of critical ill patients with sepsis and cancer: a retrospective cohort study based on MIMIC-IV and eICU-CRD.
    Yuan ZN; Xue YJ; Wang HJ; Qu SN; Huang CL; Wang H; Zhang H; Xing XZ
    BMJ Open; 2023 Sep; 13(9):e072112. PubMed ID: 37696627
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Factor analysis based on SHapley Additive exPlanations for sepsis-associated encephalopathy in ICU mortality prediction using XGBoost - a retrospective study based on two large database.
    Guo J; Cheng H; Wang Z; Qiao M; Li J; Lyu J
    Front Neurol; 2023; 14():1290117. PubMed ID: 38162445
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database.
    Wang Z; Zhang L; Li S; Xu F; Han D; Wang H; Huang T; Yin H; Lyu J
    BMC Infect Dis; 2022 Jul; 22(1):629. PubMed ID: 35850582
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers.
    Zhang G; Shao F; Yuan W; Wu J; Qi X; Gao J; Shao R; Tang Z; Wang T
    Eur J Med Res; 2024 Mar; 29(1):156. PubMed ID: 38448999
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning-based in-hospital mortality risk prediction tool for intensive care unit patients with heart failure.
    Chen Z; Li T; Guo S; Zeng D; Wang K
    Front Cardiovasc Med; 2023; 10():1119699. PubMed ID: 37077747
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis.
    Zhao QY; Liu LP; Luo JC; Luo YW; Wang H; Zhang YJ; Gui R; Tu GW; Luo Z
    Front Med (Lausanne); 2020; 7():637434. PubMed ID: 33553224
    [No Abstract]   [Full Text] [Related]  

  • 16. A machine learning-based prediction model for in-hospital mortality among critically ill patients with hip fracture: An internal and external validated study.
    Lei M; Han Z; Wang S; Han T; Fang S; Lin F; Huang T
    Injury; 2023 Feb; 54(2):636-644. PubMed ID: 36414503
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Predicting Successful Weaning from Mechanical Ventilation by Reduction in Positive End-expiratory Pressure Level Using Machine Learning.
    Sheikhalishahi S; Kaspar M; Zaghdoudi S; Sander J; Simon P; Geisler BP; Lange D; Hinske LC
    PLOS Digit Health; 2024 Mar; 3(3):e0000478. PubMed ID: 38536802
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Development and Validation of a Dynamic Nomogram for Predicting in-Hospital Mortality in Patients with Acute Pancreatitis: A Retrospective Cohort Study in the Intensive Care Unit.
    Zou K; Huang S; Ren W; Xu H; Zhang W; Shi X; Shi L; Zhong X; Peng Y; Lü M; Tang X
    Int J Gen Med; 2023; 16():2541-2553. PubMed ID: 37351008
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Machine learning models to predict in-hospital mortality in septic patients with diabetes.
    Qi J; Lei J; Li N; Huang D; Liu H; Zhou K; Dai Z; Sun C
    Front Endocrinol (Lausanne); 2022; 13():1034251. PubMed ID: 36465642
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.
    Lim L; Gim U; Cho K; Yoo D; Ryu HG; Lee HC
    Crit Care; 2024 Mar; 28(1):76. PubMed ID: 38486247
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.