ArticleQuantitative bio-science2017
A Selective Review on Random Survival Forests for High Dimensional Data.
Article in Quantitative bio-science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.Journal of medical systems · 2024Pooled it
- Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson's disease.Lifetime data analysis · 2026Article
- Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2026Article
- Integrative analysis and experimental validation of dioxin-interacting genes reveal diagnostic and prognostic biomarkers in lung adenocarcinoma.Clinical and experimental medicine · 2026Article
- Bridging survival analysis and machine learning to improve healthy life expectancy estimation using PHR records.NPJ digital medicine · 2026Article
- Machine-Learning-Based Survival Prediction in Glioblastoma Using Graph-Theoretical Analysis of Structural Network Alterations.Cancers · 2026Article
- Association of surgical resection with survival in retroperitoneal leiomyosarcoma based on SEER propensity score matching and machine-learning models.Scientific reports · 2026Article
- Artificial intelligence applications in oxaliplatin-based chemotherapy for colon cancer: advancing prognosis, toxicity prediction, and dose personalization.Frontiers in pharmacology · 2026Review
- Dual molecular and clinical machine-learning prognostic modeling in pancreatic ductal adenocarcinoma: a chaperone-mediated autophagy-based framework integrating multi-cohort molecular signatures and a single-center clinical nomogram.Frontiers in cell and developmental biology · 2026Article
- A tale of two management programs: Insights from a state-line wildlife disease outbreak.PNAS nexus · 2025Article
- Random survival forests for the analysis of recurrent events for right-censored data, with or without a terminal event.BMC medical research methodology · 2025Article
- Feature Selection and Model Optimization for Survival Prediction in Patients with Angina Pectoris.Journal of clinical medicine · 2025Article
- Machine learning prediction of overall survival in patients with cT1b renal cell carcinoma after surgical resection using the SEER database.Scientific reports · 2025Article
- Distant Metastatic Pattern and Its Prognostic Significance in Malignant Pleural Mesothelioma: A Population-Based Study Based on a Machine Learning Model.The clinical respiratory journal · 2025Article
- Predictive Analysis for First Submission of Generic Drug Application for Orphan Drug Products Using Random Survival Forest.Clinical and translational science · 2025Article
- Comparison of random survival forest and Cox regression for long-term prognosis of pediatric Wilms tumor: based on the SEER database and external validation cohort from China.Discover oncology · 2025Article
- Microbiome-based prediction of allogeneic hematopoietic stem cell transplantation outcome.Genome medicine · 2025Article
- Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort.BMC gastroenterology · 2025Article
- Machine Learning Model Predicts Abnormal Lymphocytosis Associated With Chronic Lymphocytic Leukemia.JCO clinical cancer informatics · 2025Observational
- Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.Computers in biology and medicine · 2025Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Over the past decades, there has been considerable interest in applying statistical machine learning methods in survival analysis. Ensemble based approaches, especially random survival forests, have been developed in a variety of contexts due to their high precision and non-parametric nature. This article aims to provide a timely review on recent developments and applications of random survival forests for time-to-event data with high dimensional covariates. This selective review begins with an introduction to the random survival forest framework, followed by a survey of recent developments on splitting criteria, variable selection, and other advanced topics of random survival forests for time-to-event data in high dimensional settings. We also discuss potential research directions for future research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.