Improves random survival forest model by weighted averaging.
problem Improving the performance of random survival forest.
method Modifies random forest by weighted averaging of trees, optimizing weights via quadratic optimization to maximize Harrell's C-index.
result The weighted random survival forest outperforms the original model in numerical examples.
ggRandomForests package visualizes random forest models for survival data.
problem Analyzing time to event data using random forest methods.
method Unified random forest package for survival analysis with visualizations.
result Demonstrates the strength of random forest methods for prediction and information retrieval in time to event data.
Study examines survival models for ALS, focusing on proportional hazards assumption.
problem Impact of proportional hazards assumption on survival models for ALS.
method Theoretical and empirical investigation of survival forests and their variants.
result Alternative split procedures can improve model performance in non-proportional hazards situations.
A new method selects variables for random survival forests using maximally selected rank statistics.
problem Random survival forests can be biased in selecting variables, especially for non-linear effects.
method Use maximally selected rank statistics for variable selection in random survival forests, comparing on p-value scale.
result The new method outperforms other approaches in prediction performance and computational speed.
The paper compares Bayesian trees, Cox models, and random forests for breast cancer survival data.
problem Modeling survival data with nonlinear and additive effects.
method Bayesian Additive Regression Trees, Cox proportional hazards, and Random Survival Forests.
result Bayesian trees outperform other models in terms of bias and prediction accuracy.
New methods estimate survival functions with time-varying covariates.
problem Estimating survival functions with time-varying covariates.
method Generalized conditional inference and relative risk forests, adapted transformation forest.
result Proposed methods outperform traditional models in estimating survival functions.
RSF models censored functional data for better survival analysis.
problem Modeling survival trajectories with censored data.
method Random Survival Forest for Censored Functional Data (RSF).
result Good performance in predicting survival variables.
Machine learning improves kidney transplant outcomes prediction.
problem Improving prediction of kidney transplant success.
method Random forest machine learning model trained on kidney donor risk index data.
result Random forest predicted 2,148 more successful transplants than the risk index.
BRSF improves survival model accuracy for imbalanced data.
problem Survival model accuracy compromised by extreme imbalance and right censored data.
method Balanced random survival forests (BRSF) trained with synthetic minority sampling.
result BRSF provides improved discriminatory strength and reduced prediction error.
Optimal survival trees ensemble reduces tree count and improves predictive performance.
problem Improving predictive performance in survival analysis.
method Grows a forest of optimal survival trees by ranking and selecting the best trees based on out-of-bag error.
result Reduces the number of trees in the ensemble while improving predictive performance.
Enhances interpretability of functional survival models.
problem Lack of interpretability in functional survival models limits practical use.
method Introduces novel methods to enhance interpretability of FST and explainability of FRSF.
result Proposed methods yield efficient, easy-to-understand decision trees.
Paper proposes a method to estimate confidence bands for survival random forests.
problem No statistically valid and computationally feasible approach for estimating confidence bands for survival random forests.
method Extending recent developments in infinite-order incomplete U-statistics, the paper proposes an unbiased confidence band estimation.
result The proposed method accurately estimates the confidence band and achieves desired coverage rate.
GCQRF predicts survival quantiles without linearity assumptions.
problem Survival analysis with right censoring and nonlinearity.
method Global Censored Quantile Random Forest (GCQRF) for complex relationships.
result GCQRF outperforms existing methods in predictive accuracy.
Kernel Induced Random Survival Forests (KIRSF) is a statistical learning algorithm which aims to improve prediction accuracy for survival data. As in Random Survival Forests (RSF), Cumulative Hazard Function is predicted for each individual in the test set. Prediction error is estimated using Harrell's concordance inde…
The paper predicts brain tumor patient survival using segmentation and features.
problem Predicting overall survival of brain tumor patients.
method Automated brain tumor segmentation and use of age, shape, and volumetric features for prediction.
result Random forest classifier achieves 59% accuracy on test dataset and 67% on gross total resection dataset.
This paper enhances the Random Survival Forest model for better predictive maintenance.
problem Improving time-to-failure estimation in predictive maintenance with Random Survival Forest.
method A three-level framework for quantifying hyperparameter tunability, including model-level and hyperparameter-level metrics.
result Hyperparameter tuning significantly improves Random Survival Forest model performance in predictive maintenance.
Transformed SEER data for better survival curve predictions.
problem Improving survival curve predictions for cancer patients.
method Applied a data transformation to SEER data, used machine learning models (Random Forest and Multi-Layer Perceptron).
result Models predict survival curves with AUC values from .765 to .885.
Random Forest kernels improve performance in various regression and survival tasks.
problem Improving performance of Random Forest in high-dimensional data with noisy features.
method Developed and evaluated data-driven RF kernels for regression, classification, and survival tasks.
result RF kernels are competitive or superior to RF in most scenarios, especially for survival tasks.
This paper introduces tools to predict individual survival probabilities across all times.
problem Lack of tools to provide individual survival probabilities across all time points.
method Develops and evaluates new models including extensions to Cox model, Accelerated Failure Time, Random Survival Forests, and Multi-Task Logistic Regression.
result Introduces D-Calibration for evaluating individual survival distribution models.
The paper estimates personalized treatment effects in medical settings with competing risks.
problem Estimating treatment effectiveness for specific events in the presence of alternative event types.
method Meta-learners combining Cox regression or random survival forests for risk modeling and elastic net regression or random forests for direct CATE modeling.
result Compared meta-learners in multiple simulation settings, providing practical guidance for model selection.
Study compares Cox model and RSF for predicting patient survival, finding RSF superior in certain scenarios.
problem Comparing predictive accuracy of Cox proportional hazards model and Random Survival Forest for patient-specific survival probabilities.
method Conducted a comprehensive comparison study using simulation scenarios and real-world datasets.
result RSF outperforms Cox model in nonproportional hazards settings and with treatment-covariate interactions.
The paper uses neural networks to segment brain tumors and predict patient survival.
problem Brain tumor segmentation and survival prediction.
method Fully convolutional neural network with encoder-decoder architecture, radiomic features, and random forest regression.
result 55.4% classification accuracy for predicting survival of patients with gross-total resection.
The paper explores variations of ensemble methods for cancer survival prediction.
problem Predicting survival functions in cancer research with competing risks and right censoring.
method The authors implement and compare different variations of adaboost for survival prediction.
result The variations of adaboost show improved performance in terms of run time and error metrics.
Bayesian model for survival analysis using Gaussian processes.
problem Handling various types of censoring and nonparametric modeling of survival data.
method Semi-parametric Bayesian model with a Gaussian process for nonparametric variation.
result Model performs better than competing methods on synthetic and real data.
Survival forests estimate treatment effects with censored data.
problem Estimating treatment effects in survival analysis with censored data.
method Causal survival forests using orthogonal estimating equations.
result Survival forests perform well relative to baselines in treatment effect estimation.
DynForest R package predicts outcomes with time-dependent predictors.
problem Handling time-dependent predictors in random forest models.
method Random forests with time-dependent predictors summarized using flexible linear mixed models.
result DynForest can predict continuous, categorical, and survival outcomes.
Random forest models predict CLABSI risk in hospital admissions, with static models performing similarly to dynamic ones.
problem Predicting CLABSI risk in hospital admissions using EHR data with competing risks.
method Comparison of static and dynamic random forest models for binary, multinomial, survival, and competing risks outcomes.
result Static and dynamic random forest models perform similarly in predicting CLABSI risk, with multinomial models having the lowest computation times.
We introduce the C++ application and R package ranger. The software is a fast implementation of random forests for high dimensional data. Ensembles of classification, regression and survival trees are supported. We describe the implementation, provide examples, validate the package with a reference implementation, and …
Paper proposes a new combined regression strategy for conditional survival prediction.
problem Improving survival prediction accuracy using conditional survival function.
method Uses regression-based weak learners with area-norm proximity measure to create an ensemble technique.
result The proposed model outperforms Random Survival Forest and selects important variables effectively.
Nonasymptotic error bounds and strong consistency rates for survival analysis methods.
problem Establishing reliable error bounds and consistency rates for survival analysis methods.
method Nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators in metric spaces.
result Rates of strong consistency match existing lower bounds for conditional CDF estimation.
Framework detects out-of-distribution inputs in regression and survival analysis.
problem Limited OOD detection for regression and survival analysis.
method Model-aware and subspace-aware variable prioritization.
result Consistent improvements over existing methods in synthetic and real data.
Random Forests [Breiman:2001] (RF) are a fully non-parametric statistical method requiring no distributional assumptions on covariate relation to the response. RF are a robust, nonlinear technique that optimizes predictive accuracy by fitting an ensemble of trees to stabilize model estimates. The randomForestSRC packag…
SurvBESA predicts survival times using ensemble methods with self-attention.
problem Challenges in survival analysis due to censored data and unstable predictions.
method SurvBESA combines Beran estimators with a self-attention mechanism to predict survival times.
result SurvBESA outperforms state-of-the-art models in predicting survival times.
Neural network learns kernel functions for survival analysis and prediction intervals.
problem Predicting survival times for individuals based on similar training subjects.
method Develops a neural network framework to learn kernel functions for kernel survival analysis and uses these to construct valid prediction intervals.
result Neural network survival estimators are competitive with existing methods and provide valid prediction intervals.
Improves random forest quantile estimation and prediction intervals.
problem Excessive bias in quantile estimates from random forests.
method Minimizes quantile coverage loss (QCL) by adjusting RF parameters.
result QCL-tuned RFs produce more accurate and narrower prediction intervals.
Proposes modifications to model-based forests for HTE estimation in observational data.
problem Estimating heterogeneous treatment effects in observational studies with complex outcomes.
method Orthogonalization strategy from Robinson (1988) applied to model-based forests.
result The orthogonalization strategy reduces confounding effects in simulated studies.
Efficient oblique RSF method improves prediction and interpretability.
problem Limited computational efficiency and difficulty in interpreting oblique RSF ensembles.
method Newton-Raphson scoring for computational efficiency and negation importance for variable importance estimation.
result The method reduces computational overhead by 450 times and improves prediction accuracy.
Random survival forests (RSF) are a powerful method for risk prediction of right-censored outcomes in biomedical research. RSF use the log-rank split criterion to form an ensemble of survival trees. The most common approach to evaluate the prediction accuracy of a RSF model is Harrell's concordance index for survival d…
DynForest predicts event probabilities from longitudinal data, handling endogenous predictors.
problem Predicting individual risk using longitudinal patient history.
method Random survival forests with time-fixed features from longitudinal predictors.
result DynForest provides accurate individual event probability predictions.
Study predicts colorectal polyp recurrence using medical records and statistical models.
problem Identifying patient characteristics influencing colorectal polyp recurrence.
method Natural language processing for extracting polyp characteristics, Kaplan-Meier curves, Cox proportional hazards modeling, random survival forest models.
result Polyp size, number, location, and patient smoking status significantly influence recurrence risk.
Outcome model approach improves translation of RCT results to target populations.
problem Improving translation of RCT results to real-world populations.
method Developed separate outcome models using Random Survival Forests for each treatment.
result Treatment effect estimates for valsartan and nateglinide were more efficient and accurate compared to traditional weighting approach.
Comprisk simplifies competing-risks analysis in Python.
problem Analyzing medical time-to-event data with competing risks.
method A scikit-learn-compatible toolkit for competing-risks survival analysis.
result Comprisk provides a unified API for various competing-risks methods.
Bayesian fusion forest combines clinical and real-world data to estimate treatment effects.
problem Estimating treatment effects on survival from mixed data sources with confounding.
method Nonparametric Bayesian fusion forest combining randomised and real-world data, relaxing unconfoundedness assumption.
result Fusion identifies treatment benefits not seen in clinical trials alone.
Federated survival analysis outperforms local and centralized training, with RSF offering the best balance of discrimination, calibration, and robustness.
problem Survival analysis models require large, diverse cohorts but are limited by privacy regulations and lack of centralized data.
method Federated learning (FL) is used to train shared models without exchanging raw data.
result FL consistently outperforms local training and approaches, and occasionally exceeds centralized performance.
The DeepSurv model predicts purchase timing better than other survival models.
problem Predicting the exact purchase timing of consumers.
method Survival models (Kernel SVM, DeepSurv, Survival Random Forest, MTLR) were compared using various consumer attributes.
result DeepSurv model outperformed other models in predicting purchase completion.
Predicting survival in PUBG game with machine learning models.
problem Predicting the final placement in the game PUBG.
method Applied multiple machine learning models including LightGBM Regression, MultiLayer Perceptron, M5P, and Random Forest to predict winPlacePerc.
result Achieved an MAE of 0.02047, 0.065, 0.0592, and 0.0634 with different models.
Surv-IPTB estimates patient-specific treatment benefits using survival data.
problem Estimating individual treatment benefits in survival analysis.
method Attention-based framework for binary classification of treatment effects.
result Surv-IPTB outperforms existing methods in complex survival scenarios.
Study evaluates multi-omics data's role in predicting cancer survival.
problem Determining the usefulness of multi-omics data for predicting disease outcomes.
method 5-fold cross-validation with 12 prediction methods applied to 18 cancer datasets.
result Multi-omics data generally improves prediction performance, but not consistently.