BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

583 related articles for article (PubMed ID: 35846312)

  • 1. A Novel Composite Indicator of Predicting Mortality Risk for Heart Failure Patients With Diabetes Admitted to Intensive Care Unit Based on Machine Learning.
    Yang B; Zhu Y; Lu X; Shen C
    Front Endocrinol (Lausanne); 2022; 13():917838. PubMed ID: 35846312
    [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. 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]  

  • 4. The prediction of in-hospital mortality in chronic kidney disease patients with coronary artery disease using machine learning models.
    Ye Z; An S; Gao Y; Xie E; Zhao X; Guo Z; Li Y; Shen N; Ren J; Zheng J
    Eur J Med Res; 2023 Jan; 28(1):33. PubMed ID: 36653875
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit.
    Huang T; Le D; Yuan L; Xu S; Peng X
    PLoS One; 2023; 18(1):e0280606. PubMed ID: 36701342
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. [Construction of a predictive model for in-hospital mortality of sepsis patients in intensive care unit based on machine learning].
    Zhu M; Hu C; He Y; Qian Y; Tang S; Hu Q; Hao C
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2023 Jul; 35(7):696-701. PubMed ID: 37545445
    [TBL] [Abstract][Full Text] [Related]  

  • 9. [Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].
    Xie Z; Jin J; Liu D; Lu S; Yu H; Han D; Sun W; Huang M
    Zhonghua Wei Zhong Bing Ji Jiu Yi Xue; 2024 Apr; 36(4):345-352. PubMed ID: 38813626
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Predicting Mortality in Intensive Care Unit Patients With Heart Failure Using an Interpretable Machine Learning Model: Retrospective Cohort Study.
    Li J; Liu S; Hu Y; Zhu L; Mao Y; Liu J
    J Med Internet Res; 2022 Aug; 24(8):e38082. PubMed ID: 35943767
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Machine Learning for Predicting Risk and Prognosis of Acute Kidney Disease in Critically Ill Elderly Patients During Hospitalization: Internet-Based and Interpretable Model Study.
    Li M; Han S; Liang F; Hu C; Zhang B; Hou Q; Zhao S
    J Med Internet Res; 2024 May; 26():e51354. PubMed ID: 38691403
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit.
    Wang B; Li Y; Tian Y; Ju C; Xu X; Pei S
    Respir Med; 2023 Oct; 217():107363. PubMed ID: 37451647
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning-based prediction of in-hospital mortality for critically ill patients with sepsis-associated acute kidney injury.
    Gao T; Nong Z; Luo Y; Mo M; Chen Z; Yang Z; Pan L
    Ren Fail; 2024 Dec; 46(1):2316267. PubMed ID: 38369749
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Machine learning prediction models and nomogram to predict the risk of in-hospital death for severe DKA: A clinical study based on MIMIC-IV, eICU databases, and a college hospital ICU.
    Xie W; Li Y; Meng X; Zhao M
    Int J Med Inform; 2023 Jun; 174():105049. PubMed ID: 37001474
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A time-incorporated SOFA score-based machine learning model for predicting mortality in critically ill patients: A multicenter, real-world study.
    Liu Y; Gao K; Deng H; Ling T; Lin J; Yu X; Bo X; Zhou J; Gao L; Wang P; Hu J; Zhang J; Tong Z; Liu Y; Shi Y; Ke L; Gao Y; Li W
    Int J Med Inform; 2022 Jul; 163():104776. PubMed ID: 35512625
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure.
    Luo C; Zhu Y; Zhu Z; Li R; Chen G; Wang Z
    J Transl Med; 2022 Mar; 20(1):136. PubMed ID: 35303896
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 30.