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.
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.
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.
A method for predicting survival using neural networks for both continuous and discrete time.
problem Survival prediction for both continuous and discrete time data.
method Proposes a scheme for discretizing continuous-time data and two interpolation schemes for continuous-time survival estimates.
result The hazard rate parametrization of neural networks yields better performance than the parametrization of the probability mass function.
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.
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.
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.
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.
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.
SurvFM-RMST converts survival outcomes into pseudo-observation targets for tabular models.
problem Right-censored follow-up prevents direct use of survival labels in tabular patient data.
method SurvFM-RMST framework that converts survival outcomes into jackknife pseudo-observation targets for restricted mean survival time.
result SurvFM-RMST accurately recovered restricted event-free time in simulations and outperformed naive targets in static datasets.
Deep learning predicts kidney graft survival better than traditional methods.
problem Improving survival analysis for kidney graft patients.
method Deep learning method that jointly predicts survival times and ranks.
result Deep learning outperforms Cox Proportional Hazards and other methods.
FinSurvival provides a large-scale financial survival modeling benchmark.
problem Lack of large-scale, realistic, and freely available datasets for benchmarking AI survival models.
method Derived 16 survival modeling tasks from cryptocurrency lending data using an automated pipeline.
result Demonstrated that existing AI survival models are not well-suited for these challenging tasks.
Enhances patient failure prediction using dynamic survival models.
problem Lack of precise individual level prediction in conventional models.
method Developed counterfactual dynamic survival model (CDSM).
result Inflection point of estimated survival curves predicts patient failure time.
auton-survival simplifies survival analysis for healthcare data.
problem Handling censored time-to-event data in healthcare.
method Open-source package for survival regression, adjustment, counterfactual estimation, phenotyping, and treatment effects.
result Demonstrates auton-survival's ability to support complex health and epidemiological questions.
GBST model improves credit risk quantification using survival analysis.
problem Quantifying credit risk in heterogeneous consumer finance data.
method Gradient boosting survival tree (GBST) model integrating survival analysis and gradient boosting.
result GBST model outperforms existing survival models in credit risk quantification.
Efficiently clusters survival curves without computationally intensive resampling.
problem Identifying clusters of survival curves efficiently and scalably.
method Log-rank test combined with k-means clustering.
result Achieves comparable results to bootstrap-based methods but with improved efficiency.
PyDTS analyzes survival data with discrete intervals and competing risks.
problem Discrete-time survival analysis with competing risks and optional penalization.
method Regularized estimation methods, model evaluation metrics, variable screening tools, and simulation module.
result Supports research and development in discrete-time survival analysis.
Bayesian approach clusters survival data for better risk prediction.
problem Identifying subpopulations with distinct risk profiles in survival analysis.
method Bayesian nonparametric approach in a clustered latent space.
result Consistent improvements in predictive performance and interpretability.
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.
Paper proposes robust methods for estimating optimal treatment rules with censored survival data.
problem Estimating optimal treatment rules for censored survival data.
method Developed two robust criteria and a sampling-based difference-of-convex algorithm for learning optimal treatment rules.
result Proposed methods show improved performance compared to existing methods in simulations and real data.
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.
A new method clusters survival data using deep variational models.
problem Clustering survival data, a challenging task.
method Semi-supervised probabilistic approach with deep generative model.
result The method outperforms previous models in clustering and predicting survival times.
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.
A new model calibrates survival predictions for better risk assessment.
problem Calibrated time-to-event predictions are crucial but underexplored.
method Survival function estimator using neural network draws, without adversarial learning.
result The model outperforms existing approaches in calibration and distribution concentration.
SAFE detects fraudsters in advance by predicting survival probabilities.
problem Detecting fraudsters in time given their activity sequences.
method Survival analysis with RNN to map user activities to hazard values.
result SAFE outperforms existing models in fraud early detection.
CENNSurv models cumulative effects of time-dependent exposures on survival outcomes.
problem Challenges in modeling cumulative effects of time-dependent exposures on survival outcomes.
method CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data.
result CENNSurv reveals multi-year lagged and short-term behavioral shifts in survival outcomes.
Investment strategies ensure wealth bounded away from zero in a competitive market.
problem Ensuring wealth bounded away from zero in a competitive investment market.
method Stochastic game-theoretic model with survival strategies.
result Survival strategies are asymptotically equivalent and allow faster wealth accumulation.
New method generates synthetic survival data by conditioning on event times and censoring indicators.
problem Generating accurate synthetic survival data with censored event times.
method Conditioning covariates on event times and censoring indicators using existing tabular data generation models.
result Our method consistently outperforms baselines and improves survival model performance.
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.
BoXHED2.0 boosts survival analysis for complex data.
problem Survival analysis with time-dependent covariates.
method Tree-boosted hazard estimator, fully nonparametric, scalable.
result Scalable to parametric boosted survival models in speed.
TV-SurvCaus improves causal inference for dynamic treatments in survival analysis.
problem Estimating causal effects of time-varying treatments on survival outcomes.
method Representation balancing techniques extended to time-varying treatment regimes with survival outcomes.
result TV-SurvCaus outperforms existing methods in estimating individualized treatment effects with time-varying covariates and treatments.
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.
This paper analyzes expected survival time for robust optimization in Gaussian dynamics.
problem Understanding how expected survival time depends on environmental dynamics and problem characteristics.
method Modeling survival as a discrete first-exit problem, deriving lower and upper bounds.
result Expected survival time scales as Θ(σ^-{2}) in slowly varying environments and approaches 1 in high dimensions.
Paper tackles survival data analysis with positive and unlabeled observations.
problem Traditional survival analysis yields biased results with positive-unlabeled data.
method Developed parametric, nonparametric, and machine learning models for positive and unlabeled survival data.
result Proposed estimation method provides valid results for positive-unlabeled survival data.
A new neural network model for survival analysis.
problem Applying neural networks to medical survival data.
method Discrete-time survival model using SGD for training.
result Rapid convergence and scalability for large datasets.
SSPN uses deep learning to estimate risk scores in survival analysis with competing risks.
problem Nonidentifiability of cause-specific survival curves in competing risk survival analysis.
method Siamese Survival Prognosis Network (SSPN) that avoids estimating cause-specific survival curves and optimizes an approximation to the C-discrimination index.
result SSPN estimates pairwise concordant time-dependent risks, improving risk scoring in survival analysis with competing risks.
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.
SAVAE uses deep learning for survival analysis, improving model performance and interpretability.
problem Complex medical data with censoring and covariate interactions.
method SAVAE is a Variational Autoencoder tailored for survival analysis, using a novel ELBO formulation.
result SAVAE outperforms state-of-the-art techniques in various datasets, demonstrating robustness and interpretability.
Deep Recurrent Survival Analysis models for better event prediction and survival rate estimation.
problem Survival analysis challenges in handling data censorship and sequential patterns.
method Combines deep learning for conditional probability prediction and survival analysis for censorship handling.
result Significantly outperforms state-of-the-art solutions in various metrics on real-world tasks.
Deep survival analysis improves risk stratification in EHR data.
problem Improving risk stratification in EHR data for clinical decision support.
method Hierarchical generative approach to survival analysis, modeling all observations jointly conditioned on a latent structure and aligned by failure time.
result Deep survival analysis significantly outperforms the Framingham CHD risk score in stratifying patients' risk of CHD.
SurvCORN predicts survival curves using conditional ordinal ranking networks.
problem Challenges in survival analysis with censored data.
method SurvCORN: Conditional Ordinal Ranking Neural Network.
result SurvCORN improves accuracy in predicting time-to-event outcomes.
Enhances survival analysis by separating population behavior from individual dynamics.
problem Improving the training and inference of survival analysis models for sparsely occurring events.
method Decouples survival analysis into an aggregated baseline hazard and independent survival scores.
result Achieves competitive performance and robust results without fine-tuning.
New method estimates survival risks without strong proportional hazard assumptions.
problem Time-to-event prediction with censored data and competing risks.
method Jointly learns deep nonlinear representations for fully parametric survival regression.
result Demonstrates benefits in real-world datasets with different censoring levels.
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.
Deep learning improves survival analysis for complex data types.
problem Limited application of DL in survival analysis for complex data.
method Comprehensive review of DL methods for time-to-event analysis.
result Methods often ignore complex settings like multiple risks and censoring.
NeuralSurv models survival analysis with Bayesian uncertainty.
problem Capturing time-varying risk relationships in survival analysis.
method Two-stage data-augmentation scheme, mean-field variational algorithm, coordinate-ascent updates, locally linearized Bayesian neural network.
result Delivers superior calibration compared to state-of-the-art models.
DeepHazard uses neural networks to predict time-varying survival risks.
problem Traditional survival models assume proportional hazards and do not account for time-varying covariate information.
method DeepHazard is a neural network approach that models time-varying hazards without proportional hazards assumption.
result DeepHazard outperforms existing methods in predicting survival time, as shown by C-index metrics on real datasets.