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 *

519 related articles for article (PubMed ID: 34277655)

  • 1. Machine Learning Prediction Models for Mechanically Ventilated Patients: Analyses of the MIMIC-III Database.
    Zhu Y; Zhang J; Wang G; Yao R; Ren C; Chen G; Jin X; Guo J; Liu S; Zheng H; Chen Y; Guo Q; Li L; Du B; Xi X; Li W; Huang H; Li Y; Yu Q
    Front Med (Lausanne); 2021; 8():662340. PubMed ID: 34277655
    [No Abstract]   [Full Text] [Related]  

  • 2. [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]  

  • 3. 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]  

  • 4. [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]  

  • 5. An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.
    Wen C; Zhang X; Li Y; Xiao W; Hu Q; Lei X; Xu T; Liang S; Gao X; Zhang C; Yu Z; Lü M
    PLoS One; 2024; 19(5):e0303469. PubMed ID: 38768153
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning.
    Sun Y; He Z; Ren J; Wu Y
    BMC Anesthesiol; 2023 May; 23(1):178. PubMed ID: 37231340
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database.
    Yang S; Cao L; Zhou Y; Hu C
    J Multidiscip Healthc; 2023; 16():2625-2640. PubMed ID: 37701177
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Establishment of ICU Mortality Risk Prediction Models with Machine Learning Algorithm Using MIMIC-IV Database.
    Pang K; Li L; Ouyang W; Liu X; Tang Y
    Diagnostics (Basel); 2022 Apr; 12(5):. PubMed ID: 35626224
    [No Abstract]   [Full Text] [Related]  

  • 9. Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.
    Liu X; Hu P; Yeung W; Zhang Z; Ho V; Liu C; Dumontier C; Thoral PJ; Mao Z; Cao D; Mark RG; Zhang Z; Feng M; Li D; Celi LA
    Lancet Digit Health; 2023 Oct; 5(10):e657-e667. PubMed ID: 37599147
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 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. Machine Learning Methods for Predicting Long-Term Mortality in Patients After Cardiac Surgery.
    Yu Y; Peng C; Zhang Z; Shen K; Zhang Y; Xiao J; Xi W; Wang P; Rao J; Jin Z; Wang Z
    Front Cardiovasc Med; 2022; 9():831390. PubMed ID: 35592400
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.
    Kong G; Lin K; Hu Y
    BMC Med Inform Decis Mak; 2020 Oct; 20(1):251. PubMed ID: 33008381
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A New Risk Model based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit.
    Yan P; Huang S; Li Y; Chen T; Li X; Zhang Y; Wu H; Xu J; Xie G; Xie L; Mo G
    Curr Pharm Biotechnol; 2023; 24(13):1673-1681. PubMed ID: 36825694
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Machine Learning Prediction Models for Mortality in Intensive Care Unit Patients with Lactic Acidosis.
    Pattharanitima P; Thongprayoon C; Kaewput W; Qureshi F; Qureshi F; Petnak T; Srivali N; Gembillo G; O'Corragain OA; Chesdachai S; Vallabhajosyula S; Guru PK; Mao MA; Garovic VD; Dillon JJ; Cheungpasitporn W
    J Clin Med; 2021 Oct; 10(21):. PubMed ID: 34768540
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. Explainable time-series deep learning models for the prediction of mortality, prolonged length of stay and 30-day readmission in intensive care patients.
    Deng Y; Liu S; Wang Z; Wang Y; Jiang Y; Liu B
    Front Med (Lausanne); 2022; 9():933037. PubMed ID: 36250092
    [TBL] [Abstract][Full Text] [Related]  

  • 20. [Predicting prolonged length of intensive care unit stay
    Wu JY; Lin Y; Lin K; Hu YH; Kong GL
    Beijing Da Xue Xue Bao Yi Xue Ban; 2021 Dec; 53(6):1163-1170. PubMed ID: 34916699
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 26.