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Journal Abstract Search

152 related items for PubMed ID: 39421568

  • 21. Application of an Interpretable Machine Learning Model to Predict Lymph Node Metastasis in Patients with Laryngeal Carcinoma.
    Feng M, Zhang J, Zhou X, Mo H, Jia L, Zhang C, Hu Y, Yuan W.
    J Oncol; 2022; 2022():6356399. PubMed ID: 36411795
    [Abstract] [Full Text] [Related]

  • 22. Incorporation of a machine learning pathological diagnosis algorithm into the thyroid ultrasound imaging data improves the diagnosis risk of malignant thyroid nodules.
    Li W, Hong T, Fang J, Liu W, Liu Y, He C, Li X, Xu C, Wang B, Chen Y, Sun C, Li W, Kang W, Yin C.
    Front Oncol; 2022; 12():968784. PubMed ID: 36568189
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  • 23. Prediction of subjective cognitive decline after corpus callosum infarction by an interpretable machine learning-derived early warning strategy.
    Xu Y, Sun X, Liu Y, Huang Y, Liang M, Sun R, Yin G, Song C, Ding Q, Du B, Bi X.
    Front Neurol; 2023; 14():1123607. PubMed ID: 37416313
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  • 24. Interpretable machine learning model for early prediction of delirium in elderly patients following intensive care unit admission: a derivation and validation study.
    Tang D, Ma C, Xu Y.
    Front Med (Lausanne); 2024; 11():1399848. PubMed ID: 38828233
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  • 25. Explainable machine learning in outcome prediction of high-grade aneurysmal subarachnoid hemorrhage.
    Shu L, Yan H, Wu Y, Yan T, Yang L, Zhang S, Chen Z, Liao Q, Yang L, Xiao B, Ye M, Lv S, Wu M, Zhu X, Hu P.
    Aging (Albany NY); 2024 Mar 01; 16(5):4654-4669. PubMed ID: 38431285
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  • 26. Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study.
    Yang T, Hu Y, Pan X, Lou S, Zou J, Deng Q, Zhang Q, Zhou J, Zhu J.
    Brain Sci; 2023 Mar 26; 13(4):. PubMed ID: 37190522
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  • 29. Non-Contrasted CT Radiomics for SAH Prognosis Prediction.
    Shan D, Wang J, Qi P, Lu J, Wang D.
    Bioengineering (Basel); 2023 Aug 16; 10(8):. PubMed ID: 37627852
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  • 34. [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 16; 35(7):696-701. PubMed ID: 37545445
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  • 38. Development and validation of an interpretable machine learning for mortality prediction in patients with sepsis.
    He B, Qiu Z.
    Front Artif Intell; 2024 Jul 16; 7():1348907. PubMed ID: 39040922
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