ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2024
Accelerated and Interpretable Oblique Random Survival Forests.
Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Five-Year Risk Prediction Models for Peripheral Artery Disease in Patients With Chronic Kidney Disease.Kidney medicine · 2026Article
- Survival After Left Ventricular Assist Device: A Society of Thoracic Surgeons National Intermacs Database Risk Model Assessment.The Annals of thoracic surgery · 2026Article
- A large-scale neutral comparison study of survival models on low-dimensional data.Bioinformatics (Oxford, England) · 2026Article
- Explainable machine-learning prediction of overall and cancer-specific survival in adult triple-negative breast cancer using SEER: a comparative study of nomograms and random survival forests.Translational cancer research · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- A feature selection-based oblique hyperplane for oblique random survival forests.BMC medical research methodology · 2026Article
- Explainable artificial intelligence for personalized prognosis in pancreatic cancer: A nationwide study from Taiwan.PLOS digital health · 2026Article
- Comparison of oblique random survival forest, random survival forest, and statistical models for time-to-event data using simulation study.Scientific reports · 2025Article
- Machine learning models for predicting survival in patients of hepatocellular carcinoma with second primary malignancy.Translational cancer research · 2025Article
- External validation of a proprietary risk model for 1-year mortality in community-dwelling adults aged 65 years or older.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A comparison of random forest variable selection methods for regression modeling of continuous outcomes.Briefings in bioinformatics · 2025Article
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- Accounting for racial bias and social determinants of health in a model of hypertension control.BMC medical informatics and decision making · 2025Article
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- Developing a prediction model for cognitive impairment in older adults following critical illness.BMC geriatrics · 2024Article
- Development and validation of a machine learning-based model to predict postoperative overall survival in patients with soft tissue sarcoma: a retrospective cohort study.American journal of cancer research · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles have high prediction accuracy, but assessing many linear combinations of predictors induces high computational overhead. In addition, few methods have been developed for estimation of variable importance (VI) with oblique RSFs. We introduce a method to increase computational efficiency of the oblique RSF and a method to estimate VI with the oblique RSF. Our computational approach uses Newton-Raphson scoring in each non-leaf node, We estimate VI by negating each coefficient used for a given predictor in linear combinations, and then computing the reduction in out-of-bag accuracy. In benchmarking experiments, we find our implementation of the oblique RSF is hundreds of times faster, with equivalent prediction accuracy, compared to existing software for oblique RSFs. We find in simulation studies that "negation VI" discriminates between relevant and irrelevant numeric predictors more accurately than permutation VI, Shapley VI, and a technique to measure VI using analysis of variance. All oblique RSF methods in the current study are available in the aorsf R package, and additional supplemental materials are available online.
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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.