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 *

122 related articles for article (PubMed ID: 37023844)

  • 1. A Multidatabase ExTRaction PipEline (METRE) for facile cross validation in critical care research.
    Liao W; Voldman J
    J Biomed Inform; 2023 May; 141():104356. PubMed ID: 37023844
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data.
    Tang S; Davarmanesh P; Song Y; Koutra D; Sjoding MW; Wiens J
    J Am Med Inform Assoc; 2020 Dec; 27(12):1921-1934. PubMed ID: 33040151
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. 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]  

  • 5. 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]  

  • 6. 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]  

  • 7. Interpretable machine learning models for predicting in-hospital death in patients in the intensive care unit with cerebral infarction.
    Ouyang Y; Cheng M; He B; Zhang F; Ouyang W; Zhao J; Qu Y
    Comput Methods Programs Biomed; 2023 Apr; 231():107431. PubMed ID: 36827826
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer.
    Guo C; Pan J; Tian S; Gao Y
    J Int Med Res; 2023 Nov; 51(11):3000605231198725. PubMed ID: 37950672
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification of key predictors of hospital mortality in critically ill patients with embolic stroke using machine learning.
    Liu W; Ma W; Bai N; Li C; Liu K; Yang J; Zhang S; Zhu K; Zhou Q; Liu H; Guo J; Li L
    Biosci Rep; 2022 Sep; 42(9):. PubMed ID: 35993194
    [TBL] [Abstract][Full Text] [Related]  

  • 15. 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]  

  • 16. ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU.
    Caicedo-Torres W; Gutierrez J
    J Biomed Inform; 2019 Oct; 98():103269. PubMed ID: 31430550
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Interpretable machine learning models for predicting 90-day death in patients in the intensive care unit with epilepsy.
    She Y; Zhou L; Li Y
    Seizure; 2024 Jan; 114():23-32. PubMed ID: 38035490
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Development and validation of a model for the early prediction of the RRT requirement in patients with rhabdomyolysis.
    Liu C; Yuan Q; Mao Z; Hu P; Wu R; Liu X; Hong Q; Chi K; Geng X; Sun X
    Am J Emerg Med; 2021 Aug; 46():38-44. PubMed ID: 33714053
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Machine Learning Model for the Prediction of Hemorrhage in Intensive Care Units.
    Kang S; Park C; Lee J; Yoon D
    Healthc Inform Res; 2022 Oct; 28(4):364-375. PubMed ID: 36380433
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.