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.
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48 results for “survival function”
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.
Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forestsstat.ML
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.
SurvFD and SurvSHAP-IQ provide interpretable survival models by analyzing feature interactions.
problem Non-additivity of hazard and survival functions limits standard additive explanation methods.
method SurvFD decomposes higher-order effects into time-dependent and time-independent components, extending Shapley interactions to time-indexed functions.
result SurvFD and SurvSHAP-IQ offer a new perspective on survival explanations, explicitly characterizing feature interactions.
SurvMixClust clusters survival data and predicts individual survival curves.
problem Integrating clustering into survival analysis for precision medicine.
method SurvMixClust learns latent representations for clustering and predicts survival functions using a mixture of non-parametric experts.
result SurvMixClust creates balanced clusters with distinct survival curves, outperforming clustering baselines and competing with non-clustering models in predictive accuracy.
Survival MDN uses invertible functions to speed up survival analysis models.
problem Training neural ODEs for survival analysis is computationally expensive.
method Survival MDN applies an invertible positive function to MDN outputs.
result Survival MDN outperforms or matches other models on concordance, Brier score, and log-likelihood.
The paper predicts survival functions using random survival trees and concordance maximization.
problem Predicting conditional survival functions in right-censored data.
method The approach combines regression strategies with random survival trees and maximizes concordance.
result The proposed weighted predictor outperforms the usual survival cobra in terms of concordance.
Research uses deep learning and copulas to predict multivariate survival data.
problem Handling right-censored and correlated multivariate survival data.
method Integrates deep learning, copula functions, and survival analysis. Uses copula-based activation functions to model nonlinear dependencies.
result Enhanced prediction accuracy for multivariate survival responses.
New method uses neural networks for estimating survival functions from censored data.
problem Estimating conditional survival functions from censored time-to-event data with complex predictors.
method Generative adversarial networks leveraging self-consistent equations, without parametric assumptions.
result Established the convergence rate of the proposed estimator.
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.
Survival kernets scale deep kernel survival analysis to large datasets with interpretability and theoretical guarantees.
problem Scalable and interpretable deep kernel survival analysis for large datasets.
method Survival kernets use kernel netting for training set compression and XGBoost for warm-starting neural architecture search.
result Survival kernets achieve optimal time-dependent concordance index on various survival analysis datasets.
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.
DNAMite creates interpretable, calibrated survival analysis models.
problem Limited interpretability in survival analysis models, especially for healthcare applications.
method Feature discretization and kernel smoothing in embedding module for flexible shape functions.
result DNAMite produces calibrated shape functions interpretable as contributions to cumulative incidence function.
Optimal Insurance to Minimize the Probability of Ruin: Inverse Survival Function Formulationq-fin.RM
Optimal insurance minimizes ruin probability with non-decreasing functions.
problem Minimizing ruin probability with insurance premiums and non-decreasing functions.
method Reformulated problem with inverse survival function as control variable.
result Deductible insurance with maximum limit is optimal.
Proposes HGP to improve survival analysis models.
problem Improving survival analysis models by addressing density function changes.
method Imposes constraints on local data points by regularizing the hazard function gradient.
result HGP enhances survival analysis model performance.
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients. In this paper, we propose a deep learning method that directly models the survival function instead of estimating the hazard function to predict survival times for …
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimatorcs.LG
SurvBeX explains ML survival models using Beran estimator.
problem Interpreting predictions of machine learning survival models.
method Uses modified Beran estimator as surrogate model to compute feature impacts.
result SurvBeX minimizes mean distance between black-box and surrogate model survival functions.
The paper proposes a new method for clustering survival data using smoothed log-hazard trajectories.
problem Clustering survival data based on instantaneous risk dynamics.
method Functional Principal Component Analysis applied to B-spline smoothed log-hazard trajectories.
result The proposed method provides an interpretable representation of relative temporal risk dynamics.
Metaparametric neural networks improve survival analysis without prior knowledge.
problem Current neural networks restrict survival analysis to pre-determined times and fixed function shapes.
method Metaparametric neural network framework that extends existing methods to estimate generic functions.
result Metaparametric neural networks outperform state-of-the-art methods in capturing nonlinearities and identifying temporal patterns.
Method for explaining machine learning survival models using counterfactuals.
problem Tackles the challenge of explaining survival models in machine learning.
method Introduces a condition based on the difference of mean times to event for counterfactual explanation. Reduces the problem to a convex optimization problem for Cox models and applies Particle Swarm Optimization for other models.
result Demonstrates the effectiveness of the proposed method through numerical experiments.
Enhances Cox model for survival analysis with symbolic non-linear log-risk functions.
problem Limited interpretability and non-linearity in traditional Cox models.
method Introduces GCPH model using Kolmogorov-Arnold Networks for symbolic non-linear log-risk functions.
result GCPH achieves competitive performance and superior interpretability.
New approach makes survival analysis fairer without specifying sensitive features.
problem Ensuring fairness in survival analysis models across different subpopulations.
method Distributionally robust optimization (DRO) with sample splitting strategy.
result Converted existing survival analysis models into fair versions without specifying sensitive features.
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.
Neural network models improve survival analysis with reduced computation time.
problem Limited expressiveness of standard survival models.
method Piecewise neural network models of hazard and density functions.
result Models outperform state-of-the-art models with less computation time.
Researchers analyze the relationship between ML cost functions and the C-index in survival analysis.
problem Understanding the relationship between ML cost functions and the C-index in survival analysis.
method Provided C-index Fisher-consistency results and excess risk bounds for various cost functions in survival analysis.
result Identified conditions under which ML cost functions are consistent with the C-index.
SurvBeNIM explains survival models using neural networks.
problem Explaining predictions of survival models.
method Extends Beran estimator with neural importance functions.
result Improved explanation of survival models compared to existing methods.
SDPM models survival analysis without parametric assumptions, achieving competitive performance.
problem Estimating survival distributions from censored data with flexibility and accuracy.
method Generative model using denoising diffusion, avoiding parametric assumptions and discretization.
result SDPM achieves competitive predictive performance across various metrics.
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risksstat.ML
HACSurv models dependencies between competing risks and censoring for improved survival analysis.
problem Inaccurate survival predictions due to ignoring dependencies between competing risks and censoring.
method HACSurv uses hierarchical Archimedean copulas to model dependencies and cause-specific survival functions.
result HACSurv improves accuracy in survival predictions and captures complex risk interactions.
There has been increasing interest in modelling survival data using deep learning methods in medical research. Current approaches have focused on designing special cost functions to handle censored survival data. We propose a very different method with two steps. In the first step, we transform each subject's survival …
Interpretable survival analysis improves heart failure risk prediction.
problem Improving heart failure risk prediction using survival analysis.
method Survival stacking, ControlBurn, Explainable Boosting Machines.
result Achieves state-of-the-art performance and provides novel insights.
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Datacs.LG
New survival learners estimate heterogeneous treatment effects from time-to-event data.
problem Estimating HTEs from time-to-event data with censoring outcomes.
method Orthogonal survival learners with theoretical guarantees and custom weighting functions.
result Orthogonal survival learners provide robust and model-agnostic HTE estimation.
Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inferencestat.ML
Flexible DNN for survival data, avoiding proportional hazards assumption.
problem Survival analysis with complex interactions and non-proportional hazards.
method Partially linear DNN model with a flexible nonparametric component.
result FLEXI-Haz achieves optimal convergence rates and asymptotic efficiency.
We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not im…
TripleSurv improves survival analysis by ranking samples with time-adaptive adjustments.
problem Modeling censored time-to-event data with high accuracy and robustness.
method Introduces a time-adaptive coordinate loss function to rank samples and calibrate robustness.
result TripleSurv outperforms state-of-the-art methods on various survival datasets.
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…
SurvCaus improves survival CATE estimation using neural nets.
problem Estimating Individual Treatment Effects (ITE) in survival analysis.
method Representation balancing for counterfactual inference with neural networks.
result The proposed method outperforms baseline methods in synthetic and semisynthetic datasets.
SurvLIME-KS improves survival model explanations robustly.
problem Improving explanations of unreliable survival models.
method SurvLIME-KS combines Cox proportional hazards model and Kolmogorov-Smirnov bounds for robust optimization.
result SurvLIME-KS minimizes average distance and maximizes distance in approximating cumulative hazard functions.
Paper introduces symmetric divergence link models for probability distributions.
problem Symmetric divergence measures for probability distributions.
method Two general classes of link models: one for survival functions and another for cumulative probability distribution functions.
result Advantages of symmetric divergence measures over asymmetric measures for model averaging and feature assessment.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
problem Continuous treatment recommendation in medical settings with survival data.
method Deep Survival Dose Response Function (DeepSDRF) for learning conditional average dose response (CADR) function.
result Similar performance of recommender algorithms based on random search and reinforcement learning.
Improves survival analysis across multiple domains with machine learning.
problem Adapting survival analysis to new or rare illness types with limited labeled data.
method Introduces a new survival metric and discrepancy measure for censored data, enabling domain adaptation.
result Superb performance on target domains, better treatment recommendations, and interpretable weight matrix.
Paper introduces novel survival models for handling censored data.
problem Complex data structures and heavy censoring in survival analysis.
method Combines imprecise probability theory with attention mechanisms.
result Proposed models, especially iSurvJ, outperform traditional methods.
New estimator for survival function with missing not at random censoring indicators.
problem Estimating survival function with missing not at random censoring indicators.
method Proposes a new estimator based on a conditional copula model for the missingness mechanism.
result Provides a new method for estimating conditional survival function with MNAR censoring indicators.
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.
SurvNAM explains survival model predictions using machine learning.
problem Explaining predictions of black-box survival models.
method SurvNAM is a modified Neural Additive Model (NAM) trained with a specific loss function based on Cox model and GAM.
result SurvNAM allows local and global explanation of survival model predictions.
New reward function improves GAIL performance in task-based environments.
problem Reward bias in adversarial imitation learning.
method Proposed a new reward function to overcome existing biases.
result New reward function outperforms existing methods in task-based environments.
TabSurv adapts tabular neural networks for survival analysis.
problem Survival analysis on tabular data using deep learning methods.
method Adapts modern tabular architectures to survival analysis using Weibull distribution or non-parametric prediction. Optimizes SurvHL histogram loss function.
result TabSurv consistently outperforms classical and deep learning baselines on 10 real-world survival datasets.
NSOTree combines neural networks and trees for better survival analysis interpretability.
problem Survival analysis event prediction with interpretability.
method Proposes NSOTree integrating neural networks and trees for better interpretability.
result NSOTree improves both performance and interpretability in survival analysis.
SurvSHAP(t) explains time-dependent survival predictions from machine learning models.
problem Interpreting complex survival models for time-dependent effects.
method SHapley Additive exPlanations (SHAP) adapted for time-dependent survival predictions.
result SurvSHAP(t) detects time-dependent effects and improves variable importance detection.