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Floresta Aleatória para Sobrevivência×Estimador de Sobrevivência de Kaplan-Meier×
ÁreaAnálise de sobrevivênciaAnálise de sobrevivência
FamíliaSurvival analysisSurvival analysis
Ano de origem20081958
Autor originalIshwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S.Kaplan, E. L. & Meier, P.
TipoEnsemble machine learning survival modelNon-parametric survival estimator
Fonte seminalIshwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860. DOI ↗Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗
Outros nomesRSF, Rastgele Sağkalım Ormanı (RSF), survival random forestproduct-limit estimator, km curve, kaplan-meier sağkalım analizi
Relacionados22
ResumoRandom Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates cumulative hazard functions across hundreds of trees to produce predictions and variable importance rankings.The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.
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ScholarGateComparar métodos: Random Survival Forest · Kaplan-Meier. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare