A Modified Random Survival Forests Algorithm for High Dimensional Predictors and Self-Reported Outcomes

Abstract

We present an ensemble tree-based algorithm for variable selection in high dimensional datasets, in settings where a time-to-event outcome is observed with error. The proposed methods are motivated by self-reported outcomes collected in large-scale epidemiologic studies, such as the Women’s Health Initiative. The proposed methods equally apply to imperfect outcomes that arise in other settings such as data extracted from electronic medical records. To evaluate the performance of our proposed algorithm, we present results from simulation studies, considering both continuous and categorical covariates. We illustrate this approach to discover single nucleotide polymorphisms that are associated with incident Type II diabetes in the Women’s Health Initiative. A freely available R package icRSF (R Core Team, 2018; Xu et al., 2018) has been developed to implement the proposed methods.

Immune gene prognostic signature for disease free survival of gastric cancer Translational research of an artificial intelligence survival predictive system

“…Cox survival regression algorithm, and random survival forest algorithm, have made great progress in survival prediction [21][22][23][24][25][26]. With the supports of these advanced arti cial intelligence algorithms, we have successfully established arti cial intelligence survival predictive system to predict the mortality risk curve for an individual patient.…”

“…Arti cial intelligence algorithms were performed by Python language 3.7.2 and R software 3.5.2. Arti cial intelligence algorithms were carried out according to the original articles: Multi-task logistic regression [23,30], Cox survival regression [24], and Random survival forest [21,22]. P value < 0.05 was considered statistically signi cant.…”

“…In recent years, the development of arti cial intelligence algorithms provides more choices for the predictive studies of tumor prognosis. Multi-task logistic regression algorithm, Cox survival regression algorithm, and random survival forest algorithm have been used to improve the accuracy of predictive models and prognostic models [21][22][23][24][25][26]. Therefore the current research was devoted to explore potential immune regulatory mechanism for prognosis of GC and construct arti cial intelligence survival prediction tools for predicting individual mortality risk in different time-points.…”

Immune Gene Prognostic Signature for Disease Free Survival of Gastric Cancer: Translational Research of an Artificial Intelligence Survival Predictive System

“…However, these prognostic models can't predict the mortality risk for an individual patient. In recent years, arti cial intelligence algorithms, including Multi-task logistic regression algorithm, Cox survival regression algorithm, and random survival forest algorithm, have made great progress in survival prediction [21][22][23][24][25][26]. With the supports of these advanced arti cial intelligence algorithms, we have successfully established arti cial intelligence survival predictive system to predict the mortality risk curve for an individual patient.…”

Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms

“…Artificial intelligence algorithms were performed by Python language 3.7.2 and R software 3.5.2. Artificial intelligence algorithms were carried out according to the original articles: Cox survival regression (19), multitask logistic regression (20,21), and random survival forest (22,23). Threshold for statistically significant difference was P < 0.05.…”