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895 related items for PubMed ID: 38172962

  • 1. 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 03; 29(1):14. PubMed ID: 38172962
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  • 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 Jan 03; 9():994359. PubMed ID: 36312291
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  • 5. 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 Jan 03; 14():1290117. PubMed ID: 38162445
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  • 7. 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 06; 29(1):156. PubMed ID: 38448999
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  • 8. Early Prediction of Cardiac Arrest in the Intensive Care Unit Using Explainable Machine Learning: Retrospective Study.
    Kim YK, Seo WD, Lee SJ, Koo JH, Kim GC, Song HS, Lee M.
    J Med Internet Res; 2024 Sep 17; 26():e62890. PubMed ID: 39288404
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  • 13. 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 Sep 17; 18(1):e0280606. PubMed ID: 36701342
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  • 14. 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 09; 24(8):e38082. PubMed ID: 35943767
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  • 15. 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 Aug 09; 10():1086339. PubMed ID: 36711330
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  • 17. Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.
    Pan P, Li Y, Xiao Y, Han B, Su L, Su M, Li Y, Zhang S, Jiang D, Chen X, Zhou F, Ma L, Bao P, Xie L.
    J Med Internet Res; 2020 Nov 11; 22(11):e23128. PubMed ID: 33035175
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  • 18. Construction and validation of prognostic models in critically Ill patients with sepsis-associated acute kidney injury: interpretable machine learning approach.
    Fan Z, Jiang J, Xiao C, Chen Y, Xia Q, Wang J, Fang M, Wu Z, Chen F.
    J Transl Med; 2023 Jun 22; 21(1):406. PubMed ID: 37349774
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  • 20. 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 04; 13(1):12697. PubMed ID: 37542106
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