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Updated: Sep 11, 2025

An R-Based Landscape Validation of a Competing Risk Model
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使用极端梯度增强预测模型对COVID-19死亡预测模型的动态评估

José Carlos Prado Junior1, Alexandre Evsukoff2, Roberto de Andrade Medronho1

  • 1Instituto de Estudos de Saúde Coletiva, Faculdade de Medicina, Universidade Federal do Rio de Janeiro (UFRJ). Av. Carlos Chagas Filho 373, Cidade Universitária. 21044-020 Rio de Janeiro RJ Brasil. jcpradojr@gmail.com.

Ciencia & saude coletiva
|August 13, 2025
PubMed
概括

这项研究开发了一种Covid-19死亡预测模型,在住院患者中使用极端梯度提升 (XGBoost). 该模型准确地确定了关键的临床和实验室因素,有助于预测严重的结果.

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Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Assumptions of Survival Analysis

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End Point Prediction: Gran Plot

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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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