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Development, validation, and visualization of a web-based nomogram for predicting chronic kidney disease incidence at health examination centers
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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Prognostic Analysis of Elderly Patients with Hepatocellular Carcinoma: an Exploration and Machine Learning Model Prediction Based on Age Stratification and Surgical Approach
Published 2025-04-01“…We evaluated 147 machine learning models to establish the optimal predictive model. …”
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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
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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