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Journal Abstract Search

372 related items for PubMed ID: 37268685

  • 1. Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP.
    Alabi RO, Elmusrati M, Leivo I, Almangush A, Mäkitie AA.
    Sci Rep; 2023 Jun 02; 13(1):8984. PubMed ID: 37268685
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  • 4. An interpretable machine learning prognostic system for locoregionally advanced nasopharyngeal carcinoma based on tumor burden features.
    Chen X, Li Y, Li X, Cao X, Xiang Y, Xia W, Li J, Gao M, Sun Y, Liu K, Qiang M, Liang C, Miao J, Cai Z, Guo X, Li C, Xie G, Lv X.
    Oral Oncol; 2021 Jul 02; 118():105335. PubMed ID: 34023742
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  • 5. Machine learning-enabled prediction of prolonged length of stay in hospital after surgery for tuberculosis spondylitis patients with unbalanced data: a novel approach using explainable artificial intelligence (XAI).
    Yasin P, Yimit Y, Cai X, Aimaiti A, Sheng W, Mamat M, Nijiati M.
    Eur J Med Res; 2024 Jul 25; 29(1):383. PubMed ID: 39054495
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  • 6. 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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  • 9. Extreme gradient boosting model to assess risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual prediction using SHapley Additive exPlanations.
    Zou Y, Shi Y, Sun F, Liu J, Guo Y, Zhang H, Lu X, Gong Y, Xia S.
    Comput Methods Programs Biomed; 2022 Oct 11; 225():107038. PubMed ID: 35930861
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  • 14. Development and Interpretation of Multiple Machine Learning Models for Predicting Postoperative Delayed Remission of Acromegaly Patients During Long-Term Follow-Up.
    Dai C, Fan Y, Li Y, Bao X, Li Y, Su M, Yao Y, Deng K, Xing B, Feng F, Feng M, Wang R.
    Front Endocrinol (Lausanne); 2020 Oct 11; 11():643. PubMed ID: 33042013
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  • 16. Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan.
    Pai KC, Su SA, Chan MC, Wu CL, Chao WC.
    BMC Anesthesiol; 2022 Nov 14; 22(1):351. PubMed ID: 36376785
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  • 17. Explainable machine learning to predict long-term mortality in critically ill ventilated patients: a retrospective study in central Taiwan.
    Chan MC, Pai KC, Su SA, Wang MS, Wu CL, Chao WC.
    BMC Med Inform Decis Mak; 2022 Mar 25; 22(1):75. PubMed ID: 35337303
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  • 18. 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 Mar 25; 20():2861-2870. PubMed ID: 35765651
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