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 *

1775 related articles for article (PubMed ID: 36312291)

  • 1. 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]  

  • 2. Interpretable machine learning for predicting 28-day all-cause in-hospital mortality for hypertensive ischemic or hemorrhagic stroke patients in the ICU: a multi-center retrospective cohort study with internal and external cross-validation.
    Huang J; Chen H; Deng J; Liu X; Shu T; Yin C; Duan M; Fu L; Wang K; Zeng S
    Front Neurol; 2023; 14():1185447. PubMed ID: 37614971
    [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. 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]  

  • 5. 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]  

  • 6. Interpretable Machine Learning for Early Prediction of Prognosis in Sepsis: A Discovery and Validation Study.
    Hu C; Li L; Huang W; Wu T; Xu Q; Liu J; Hu B
    Infect Dis Ther; 2022 Jun; 11(3):1117-1132. PubMed ID: 35399146
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: Development and external validation of an interpretable machine learning model.
    Sun R; Wang X; Jiang H; Yan Y; Dong Y; Yan W; Luo X; Miu H; Qi L; Huang Z
    Front Cardiovasc Med; 2022; 9():1035675. PubMed ID: 36386374
    [TBL] [Abstract][Full Text] [Related]  

  • 8. ACEI/ARB Medication During ICU Stay Decrease All-Cause In-hospital Mortality in Critically Ill Patients With Hypertension: A Retrospective Cohort Study Based on Machine Learning.
    Yang B; Xu S; Wang D; Chen Y; Zhou Z; Shen C
    Front Cardiovasc Med; 2021; 8():787740. PubMed ID: 35097006
    [No Abstract]   [Full Text] [Related]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. 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]  

  • 15. 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]  

  • 16. Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms.
    Wang Y; Sun X; Lu J; Zhong L; Yang Z
    Ann Med; 2024 Dec; 56(1):2388709. PubMed ID: 39155811
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.
    Bu ZJ; Jiang N; Li KC; Lu ZL; Zhang N; Yan SS; Chen ZL; Hao YH; Zhang YH; Xu RB; Chi HW; Chen ZY; Liu JP; Wang D; Xu F; Liu ZL
    Medicine (Baltimore); 2024 Jul; 103(30):e38747. PubMed ID: 39058887
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. Prediction of 28-Day All-Cause Mortality in Heart Failure Patients with Clostridioides difficile Infection Using Machine Learning Models: Evidence from the MIMIC-IV Database.
    Shi C; Jie Q; Zhang H; Zhang X; Chu W; Chen C; Zhang Q; Hu Z
    Cardiology; 2024 Aug; ():1. PubMed ID: 39154641
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Twenty-eight-day in-hospital mortality prediction for elderly patients with ischemic stroke in the intensive care unit: Interpretable machine learning models.
    Huang J; Jin W; Duan X; Liu X; Shu T; Fu L; Deng J; Chen H; Liu G; Jiang Y; Liu Z
    Front Public Health; 2022; 10():1086339. PubMed ID: 36711330
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 89.