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    Development, validation, and visualization of a web-based nomogram for predicting chronic kidney disease incidence at health examination centers by Chuxuan Luo, Lanjun Fu, Lin Liu, Maosheng Chen, Kunliang Chen, Yiwen Li, Bo Lin, Juan Jin, Bin Zhu, Qiang He, Lina Shao

    Published 2024-12-01
    “…We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/).Conclusion Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.…”
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    Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment by Du S, Wu Y, Tao J, Shu L, Yan T, Xiao B, Lv S, Ye M, Gong Y, Zhu X, Hu P, Wu M

    Published 2025-03-01
    “…Age (Adjusted OR [aOR], 1.08; 95% CI: 1.03– 1.13; p = 0.002), subarachnoid hemorrhage volume (aOR, 1.02; 95% CI: 1.00– 1.05; p = 0.033), World Federation of Neurosurgical Societies grade (WFNS) (aOR, 2.03; 95% CI: 1.05– 3.93; p = 0.035), and Hunt-Hess grade (aOR, 2.36; 95% CI: 1.13– 4.93; p = 0.022) were identified as the independent risk factors of the poor outcome. Then, the prediction models developed have revealed that LightGBM algorithm has a superior performance with an AUC-ROC value of 0.842 in the validation cohort, while the SHAP results showed that age is the most important risk factor affecting functional outcomes.Conclusion: The LightGBM model holds immense potential in facilitating risk stratification for poor-grade aSAH patients undergoing endovascular treatment who are at risk of adverse outcomes, thereby enhancing clinical decision-making processes.Trial Registration: PROSAH-MPC. …”
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