Random survival forests
arXiv:0811.1645 · doi:10.1214/08-AOAS169
Abstract
We introduce random survival forests, a random forests method for the analysis of right-censored survival data. New survival splitting rules for growing survival trees are introduced, as is a new missing data algorithm for imputing missing data. A conservation-of-events principle for survival forests is introduced and used to define ensemble mortality, a simple interpretable measure of mortality that can be used as a predicted outcome. Several illustrative examples are given, including a case study of the prognostic implications of body mass for individuals with coronary artery disease. Computations for all examples were implemented using the freely available R-software package, randomSurvivalForest.
Published in at http://dx.doi.org/10.1214/08-AOAS169 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
References in corpus (2)
Cited by in corpus (80)
- Random survival forests
- DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
- A review on longitudinal data analysis with random forest in precision medicine
- Consistency of random forests
- Deep Learning for Survival Analysis: A Review
- Unbiased split variable selection for random survival forests using maximally selected rank statistics
- Student Success Prediction in MOOCs
- mlr3proba: An R Package for Machine Learning in Survival Analysis
- Large-scale benchmark study of survival prediction methods using multi-omics data
- SurvSHAP(t): Time-dependent explanations of machine learning survival models
- OmiEmbed: a unified multi-task deep learning framework for multi-omics data
- Node harvest
- Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles
- Estimating heterogeneous survival treatment effect in observational data using machine learning
- DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
- Improvement of a Prediction Model for Heart Failure Survival through Explainable Artificial Intelligence
- High-Dimensional Survival Analysis: Methods and Applications
- survex: an R package for explaining machine learning survival models
- Gradient Boosting Survival Tree with Applications in Credit Scoring
- SurvTRACE: Transformers for Survival Analysis with Competing Events
- Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data
- Bayesian nonparametric cross-study validation of prediction methods
- Opening the random forest black box by the analysis of the mutual impact of features
- Counterfactual Phenotyping with Censored Time-to-Events
- Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach
- The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models
- EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images
- CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis
- Supervised learning for the prediction of firm dynamics
- A data-driven prospective study of incident dementia among older adults in the United States
- Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach
- Enabling Counterfactual Survival Analysis with Balanced Representations
- Combining Sequential and Aggregated Data for Churn Prediction in Casual Freemium Games
- A Recurrent Neural Network Survival Model: Predicting Web User Return Time
- Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis
- Optimal Cox Regression Subsampling Procedure with Rare Events
- Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree
- A General Machine Learning Framework for Survival Analysis
- Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration
- Remembering Leo Breiman
- Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics
- Federated Survival Forests
- Neural Networks for Scalar Input and Functional Output
- Survival Function Matching for Calibrated Time-to-Event Predictions
- The Winning Solution to the IEEE CIG 2017 Game Data Mining Competition
- Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
- Survival Forests under Test: Impact of the Proportional Hazards Assumption on Prognostic and Predictive Forests for ALS Survival
- From Non-Paying to Premium: Predicting User Conversion in Video Games with Ensemble Learning
- Survival trees for right-censored data based on score based parameter instability test
- A Random Forest Approach to Identifying Young Stellar Object Candidates in the Lupus Star-Forming Region
- User Engagement in Mobile Health Applications
- Variational Learning of Individual Survival Distributions
- Evaluation of network-guided random forest for disease gene discovery
- Real-time solar image classification: assessing spectral, pixel-based approaches
- Model-based causal feature selection for general response types
- What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
- Tree models for difference and change detection in a complex environment
- Assessing variable importance in survival analysis using machine learning
- Towards the use of multiple ROIs for radiomics-based survival modelling: finding a strategy of aggregating lesions
- Efficient Training of Probabilistic Neural Networks for Survival Analysis
- BBE-LSWCM: A Bootstrapped Ensemble of Long and Short Window Clickstream Models
- An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group
- Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
- A framework for leveraging machine learning tools to estimate personalized survival curves
- Uncertainty in Lung Cancer Stage for Outcome Estimation via Set-Valued Classification
- Predicting the Lifespan of Industrial Printheads with Survival Analysis
- A Comprehensive Framework for Evaluating Time to Event Predictions using the Restricted Mean Survival Time
- Comparison of methods for analyzing environmental mixtures effects on survival outcomes and application to a population-based cohort study
- Random Survival Forest for Censored Functional Data
- Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
- Metaparametric Neural Networks for Survival Analysis
- Optimizing Prognostic Biomarker Discovery in Pancreatic Cancer Through Hybrid Ensemble Feature Selection and Multi-Omics Data
- Inference on summaries of a model-agnostic longitudinal variable importance trajectory with application to suicide prevention
- Neural Diffusion Processes for Physically Interpretable Survival Prediction
- Dual Model Deep Learning for Alzheimer Prognostication
- Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
- On the Tunability of Random Survival Forests Model for Predictive Maintenance
- Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
- A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis
- Benchmarking multi-step methods for the dynamic prediction of survival with numerous longitudinal predictors