WRSE predicts dynamic survival distributions in ICU patients.
problem Dynamic assessment of ICU patient mortality risk.
method Non-parametric weighted-resolution ensemble model combining binary classifiers.
result Competitive results with state-of-the-art models, reducing training time.
CDVI improves variational inference for survival analysis by considering censoring mechanisms.
problem Challenges in applying variational methods to survival data, especially the dependence on censoring.
method Censor-dependent variational inference (CDVI) tailored for latent variable models in survival analysis.
result Significant improvements in estimating individual survival distributions.
An accurate model of a patient's individual survival distribution can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (e.g., from Cox Proportional Hazard models) do not provide survival probabilities, single-time probability models (e.g., the Gail model, predicting 5 year prob…
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.
The paper addresses evaluating survival predictions using discrimination measures, finding a robust method to convert distributions to risks.
problem Evaluating survival distribution predictions with discrimination measures is challenging and often leads to unfair comparisons.
method The paper surveys existing methods and recommends summing over the predicted cumulative hazard as the most robust method to convert distributions to risks.
result Summing over the predicted cumulative hazard is the most robust method to convert distribution predictions to risk predictions.
Study proposes a new model for joint survival annuity valuation.
problem Valuation of joint survival annuities and options.
method Linear-rational Wishart mortality model based on stochastic matrix affine process.
result Derives closed-form expression for joint survival annuity and option.
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.
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.
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.
VSI model predicts survival distributions efficiently.
problem Predicting time-to-event distributions in clinical applications.
method Variational framework for non-parametric distribution estimation.
result Improved performance over competing solutions.
Survival regression method improves log-likelihood scores.
problem Improper scoring rules in survival regression models.
method SurvivalMonotonic-net (SuMo-net) with monotonic neural networks.
result SuMo-net achieves state-of-the-art log-likelihood scores.
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.
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.
WTNN models survival with neural networks for maintenance data.
problem Modeling survival with limited proxy data and censored observations.
method WTNN integrates neural networks with Weibull distribution for time-dependent covariates.
result WTNN produces robust survival predictions improving on existing methods.
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.
We investigate the effect of the proportional hazards assumption on prognostic and predictive models of the survival time of patients suffering from amyotrophic lateral sclerosis (ALS). We theoretically compare the underlying model formulations of several variants of survival forests and implementations thereof, includ…
Accurate models of patient survival probabilities provide important information to clinicians prescribing care for life-threatening and terminal ailments. A recently developed class of models - known as individual survival distributions (ISDs) - produces patient-specific survival functions that offer greater descriptiv…
Improves survival prediction model calibration for better individual decision-making.
problem Survival prediction's marginal and conditional calibration issues.
method Conformal prediction using individual survival probabilities.
result Effective marginal and conditional calibration without compromising discrimination.
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.
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.
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 …
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.
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.
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 method handles dependent censoring without specifying copula.
problem Dependent censoring in survival analysis.
method Deep learning with flexible copula estimation.
result Significant reduction in survival estimation bias.
Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times, or relative risk. Model calibration is relatively under explored, despite it…
Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging …
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.
SurvivalPFN simplifies survival analysis through amortized Bayesian inference.
problem Selecting appropriate survival analysis methods requires expertise and can be time-consuming.
method SurvivalPFN uses a prior-data fitted network for in-context Bayesian inference.
result SurvivalPFN achieves strong predictive performance across diverse datasets.
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.
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framework that we adapt to survival analysis on small data sets with missing values. In our model, the true health status appears as a set of late…
A new method models individual survival curves using conditional normalizing flows.
problem Precise per-individual predictions in survival analysis.
method Conditional normalizing flows for flexible and individualized survival distributions.
result Efficient estimation of individual survival curves without overfitting.
X-CAL improves survival model calibration without sacrificing predictive power.
problem Improving the calibration of survival models to better match observed data.
method Explicit calibration (X-CAL) turns distributional calibration into a differentiable objective for survival modeling.
result X-CAL improves calibration metrics without significantly reducing predictive performance.
Neural model predicts survival outcomes and reveals feature relationships.
problem Predicting time-to-event outcomes and understanding feature relationships in clinical data.
method Survival and topic modeling combined in a neural network framework.
result Neural survival-supervised topic models achieve competitive accuracy with interpretability.
Paper develops a neural network method for censored survival analysis.
problem Distribution-free quantile prediction for censored survival data.
method Develops a novel neural network algorithm for simultaneous quantile optimization.
result The algorithm produces better calibrated quantiles on real datasets.
Innovative game theory approach optimizes survival analysis metrics.
problem Survival analysis models trained with maximum likelihood do not directly optimize criteria like Brier score or Bernoulli log likelihood.
method Inverse-Weighted Survival Games: Construct objectives from re-weighted estimates featuring the other model, holding the latter fixed during training.
result Games optimize Brier score on simulations and real-world data.
This article analyzes the problem of estimating the time until an event occurs, also known as survival modeling. We observe through substantial experiments on large real-world datasets and use-cases that populations are largely heterogeneous. Sub-populations have different mean and variance in their survival rates requ…
SurvTRACE uses transformers to analyze survival times with competing events.
problem Analyzing survival times with multiple competing events and confounders.
method Transformer-based model for survival analysis, handling competing events and confounders.
result SurvTRACE outperforms existing methods in multi-event scenarios.
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.
This paper uses neural networks to accurately model competing risks in survival analysis.
problem Ignoring competing risks leads to biased survival estimation in machine learning models.
method The paper introduces constrained monotonic neural networks to model each competing survival distribution.
result The method ensures exact likelihood maximization with reduced computational cost.
Paper introduces a method to improve survival model calibration without sacrificing discrimination.
problem Survival models struggle to balance accurate ranking and event prediction.
method Uses conformal regression to enhance calibration without compromising discrimination.
result The approach improves model calibration across 11 real-world datasets.
Paper develops conformalized survival analysis method for better prediction.
problem Survival analysis models often misspecify and require strong assumptions.
method Uses conformal prediction to wrap around any survival prediction algorithm.
result Lower predictive bounds provide guaranteed coverage without strong assumptions.
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.
Proposes isotonic regression for calibrating Deep Cox models' survival probabilities.
problem Poor calibration of Deep Cox models' survival probabilities.
method Isotonic regression for post hoc calibration of Deep Cox models.
result Establishes favorable theoretical guarantees and demonstrates empirical effectiveness.
Application of discrete-time survival methods for continuous-time survival prediction is considered. For this purpose, a scheme for discretization of continuous-time data is proposed by considering the quantiles of the estimated event-time distribution, and, for smaller data sets, it is found to be preferable over the …
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.
Formulae derived for survival and first passage times in stochastic processes.
problem Computing survival and first passage times for jump and diffusion processes.
method Recursive formula derivation for nextth survival and first passage time distributions. result General formulae for nextth survival and first passage times in multi-coordinate stochastic processes. New deep Cox mixture model improves survival analysis performance.
problem Challenges in survival analysis due to censoring and healthcare applications.
method Learning mixtures of Cox regressions with deep neural networks for hazard ratios and non-parametric baseline hazard.
result Our approach outperforms classical and modern survival analysis methods, especially in minority demographics.