… machine learning methods for survival … the survival analysis methods developed in traditional
statistics, as well as those that have been proposed more recently by the machine learning …

… of risk factors influencing allograft survival. In this study, we applied machine learning methods,
in combination with survival statistics, to build new prediction models of graft survival that …

… of censored survival … machine learning algorithms, we compared all algorithms in their
ability to predict one or more of the following survival outcomes: (i) continuous: overall survival …

… statistical survival analysis, traditional machine learning, and survival machine-learning …
the results with a statistical survival analysis model and a machine learning model that fails to …

… shown that short-term survival is overestimated and long-term survival is underestimated.
Machine learning is … The SEER Program is an authoritative repository of cancer statistics in the …

… model with traditional statistics-based … for survival prediction at the individual level. In this
study, we built two recently developed machine learning models, namely, conditional survival …

… Background: Machine learning (ML) algorithms are increasingly explored in glioma …
Random survival forest (RSF) is a common ML approach in analyzing time-to-event survival data. …

… in survival analysis boosted by deep learning techniques from … survival clustering. We will
discuss the applications of statistical methods, traditional machine learning, and deep learning …

… Survival analysis is the field of statistics concerned with the estimation of time-to-event
distributions while accounting for censoring and truncation. mlr3proba introduces …

Simple Summary This article proposes a comparative study between two models that can
be used by researchers for the analysis of survival data: Weibull regression and random …