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 …
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
48 results for “Survival Rate”
We establish the first nonasymptotic error bounds for Kaplan-Meier-based nearest neighbor and kernel survival probability estimators where feature vectors reside in metric spaces. Our bounds imply rates of strong consistency for these nonparametric estimators and, up to a log factor, match an existing lower bound for c…
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.
In this paper we utilize a survival analysis methodology incorporating Bayesian additive regression trees to account for nonlinear and additive covariate effects. We compare the performance of Bayesian additive regression trees, Cox proportional hazards and random survival forests models for censored survival data, usi…
Proposes a flexible neural model for multi-state survival analysis.
problem Limited applicability of Cox models for multi-state and competing events.
method Uses neural ordinary differential equations to solve Kolmogorov forward equations.
result Demonstrates state-of-the-art performance and interpretability.
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…
MOB-dS uses permutation to correct for dependency in discrete survival data.
problem Identifying subgroups in discrete event time data with potential spurious results.
method Model-based recursive partitioning (MOB) with modified data matrix and permutation test.
result MOB-dS controls type I error rate better than standard MOB for discrete survival data.
A new model avoids the PH assumption for right-censored survival data.
problem Inflexibility of Cox model when PH assumption fails.
method Deep partially linear transformation model (DPLTM) for right-censored data.
result The DPLTM avoids the curse of dimensionality and retains interpretability.
Semi-parametric survival analysis methods like the Cox Proportional Hazards (CPH) regression (Cox, 1972) are a popular approach for survival analysis. These methods involve fitting of the log-proportional hazard as a function of the covariates and are convenient as they do not require estimation of the baseline hazard …
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.
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.
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.
Breast cancer is the most frequently reported cancer type among the women around the globe and beyond that it has the second highest female fatality rate among all cancer types. Despite all the progresses made in prevention and early intervention, early prognosis and survival prediction rates are still unsatisfactory. …
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.
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.
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.
Survival analysis of 832,941 Solana token launches shows a significant decline in graduation rate.
problem Analyzing the survival rate of Solana token launches and identifying factors affecting graduation.
method Survival analysis using Kaplan-Meier and Cox proportional-hazards models.
result The survival rate of Solana token launches has declined significantly, with a 3.18x decrease from previous rates.
An RNN-Survival model predicts optimal email send times based on recipient behavior.
problem Predicting optimal send times for emails to maximize open rates.
method Recurrent Neural Network (RNN) in a survival model framework.
result The RNN-Survival model outperforms traditional survival analysis in predicting times-to-open.
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.
Paper models treatment effects by clustering patients with distinct survival characteristics.
problem Estimating treatment efficacy in clinical settings with censored outcomes.
method Latent variable approach to model heterogeneous treatment effects.
result The latent structure can mediate base survival rates and reveal actionable phenotypes.
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.
Independent screening for single-index hazard rate models with ultra-high dimensional featuresstat.ML
In data sets with many more features than observations, independent screening based on all univariate regression models leads to a computationally convenient variable selection method. Recent efforts have shown that in the case of generalized linear models, independent screening may suffice to capture all relevant feat…
Bounds derived for contract values in life insurance with financial market interaction.
problem Incompleteness in life tables for modern insurance products.
method Derivation of upper and lower bounds for hybrid functionals of lifetime under different assumptions.
result Characterization of worst- and best-case contract values over compatible mortality processes.
KAPLAN-HR models survival data without manual interactions, outperforming existing methods.
problem Survival analysis challenges with complex covariates and time-varying effects.
method Kolmogorov-Arnold Networks (KAN) for nonparametric hazard estimation.
result KAPLAN-HR matches or exceeds existing methods in clinical survival data.
There is currently great interest in applying neural networks to prediction tasks in medicine. It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator. This avoids information loss when training the model and enables generation o…
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 …
Novel approach models life events using causal discovery and survival analysis.
problem Modeling life event choices and occurrence from a probabilistic perspective.
method Bi-level problem formulation: causal discovery for life events graph, survival analysis for time-to-event modeling.
result Identification of causal relationships and factors influencing transition rates between life events.
TRASHFIRE improves model robustness by analyzing training rates and costs.
problem Understanding and predicting model robustness under adversarial conditions.
method Survival models, worst-case examples, cost-aware analysis.
result Deeper models offer marginal robustness gains due to inference time, not inherent robustness.
Beta Survival Modelscs.LG
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…
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.
Develops a deep survival model for causal inference in longitudinal studies.
problem Estimating treatment effects on time-to-event outcomes in observational studies with time-dependent covariates.
method TCS model using potential outcomes framework and ensemble of recurrent subnetworks.
result Identifies conditional average treatment effects and individual treatment effect heterogeneity over time.
Forward transition ratesmath.PR
The idea of forward rates stems from interest rate theory. It has natural connotations to transition rates in multi-state models. The generalization from the forward mortality rate in a survival model to multi-state models is non-trivial and several definitions have been proposed. We establish a theoretical framework f…
We introduce a doubly stochastic proximal gradient algorithm for optimizing a finite average of smooth convex functions, whose gradients depend on numerically expensive expectations. Our main motivation is the acceleration of the optimization of the regularized Cox partial-likelihood (the core model used in survival an…
New method estimates optimal personalized treatment rules from mixed data sources.
problem Combining RCT and observational data for personalized treatment rules.
method Doubly robust estimator for value function, maximizing within pre-specified ITR class.
result Consistent and asymptotically normal optimal value estimator with rate of convergence.
We develop a theory for pricing non-diversifiable mortality risk in an incomplete market. We do this by assuming that the company issuing a mortality-contingent claim requires compensation for this risk in the form of a pre-specified instantaneous Sharpe ratio. We prove that our ensuing valuation formula satisfies a nu…
The goal of this paper was to predict the placement in the multiplayer game PUBG (playerunknown battleground). In the game, up to one hundred players parachutes onto an island and scavenge for weapons and equipment to kill others, while avoiding getting killed themselves. The available safe area of the game map decreas…
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn re…
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.
Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients' electronic medical recor…
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.
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.
Privacy-preserving inference for clinical trials using differential privacy.
problem Balancing knowledge sharing and privacy in healthcare data.
method Differential privacy (DP) applied to log-linear belief updates in distributed settings.
result Differentially private, distributed inference methods outperform existing techniques.
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.
RSM provides insights into deep survival models' decision-making.
problem Ensuring trust in deep survival models' predictions for healthcare applications.
method Reverse survival model (RSM) framework that explains deep survival models' decisions.
result RSM extracts relevant features for deep survival models' predictions.
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.
Reduces survival analysis to common regression tasks.
problem Applying standard machine learning tools to survival analysis.
method Various reduction techniques to simplify survival analysis.
result Benchmark analysis shows improved predictive performance.
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.
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…