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Journal Abstract Search

1020 related items for PubMed ID: 35943767

  • 21. 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 17; ():1. PubMed ID: 39154641
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  • 22. 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 Aug 17; 14():1290117. PubMed ID: 38162445
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  • 23. 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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  • 26. 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 17; 11(3):1117-1132. PubMed ID: 35399146
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  • 29. Application of interpretable machine learning for early prediction of prognosis in acute kidney injury.
    Hu C, Tan Q, Zhang Q, Li Y, Wang F, Zou X, Peng Z.
    Comput Struct Biotechnol J; 2022 Jun 17; 20():2861-2870. PubMed ID: 35765651
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  • 30. 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 17; 5(10):e657-e667. PubMed ID: 37599147
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  • 31. Explainable machine learning model to predict refeeding hypophosphatemia.
    Choi TY, Chang MY, Heo S, Jang JY.
    Clin Nutr ESPEN; 2021 Oct 17; 45():213-219. PubMed ID: 34620320
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  • 33. 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 Oct 17; 19(5):e0303469. PubMed ID: 38768153
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  • 35. SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.
    Luo H, Xiang C, Zeng L, Li S, Mei X, Xiong L, Liu Y, Wen C, Cui Y, Du L, Zhou Y, Wang K, Li L, Liu Z, Wu Q, Pu J, Yue R.
    Sci Rep; 2024 Jul 31; 14(1):17728. PubMed ID: 39085442
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  • 36. 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 01; 26():e51354. PubMed ID: 38691403
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