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Survival Analysis

Survival Analysis is a branch of statistics focused on the study of time-to-event data, usually called survival times. This type of data appears in a wide range of applications such as failure times in mechanical systems, death times of patients in a clinical trial or duration of unemployment in a population. One of the main objectives of Survival Analysis is the estimation of the so-called survival function and the hazard function. If a random variable has density function $f$ and cumulative distribution function $F$, then its survival function $S$ is $1-F$, and its hazard $λ$ is $f/S$.

Source: Gaussian Processes for Survival Analysis

Image: Kvamme et al.

Papers

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Showing 351–400 of 472 papers

TitleStatusHype
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
MENSA: A Multi-Event Network for Survival Analysis under Informative CensoringCode0
Deep Landscape Forecasting for Real-time Bidding AdvertisingCode0
Proper Scoring Rules for Survival AnalysisCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive ModelsCode0
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo ValuesCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
Dynamical Survival Analysis with Controlled Latent StatesCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
Clustering Survival Data using a Mixture of Non-parametric ExpertsCode0
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health recordsCode0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
Temporal Label Smoothing for Early Event PredictionCode0
A survey of Transformer applications for histopathological image analysis: New developments and future directionsCode0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression AnalysisCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
The Brier Score under Administrative Censoring: Problems and SolutionsCode0
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival RiskCode0
Censor Dependent Variational InferenceCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical TrialsCode0
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics DataCode0
Region-specific Risk Quantification for Interpretable Prognosis of COVID-19Code0
Calibration Error for Heterogeneous Treatment EffectsCode0
Reinterpreting survival analysis in the universal approximator ageCode0
Multi-Source Survival Domain AdaptationCode0
Research Reproducibility as a Survival AnalysisCode0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction modelsCode0
Multivariate Arrival Times with Recurrent Neural Networks for Personalized Demand ForecastingCode0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency RatesCode0
A kernel log-rank test of independence for right-censored dataCode0
Exploring the Wasserstein metric for survival analysisCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival AnalysisCode0
Neural Fine-Gray: Monotonic neural networks for competing risksCode0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatesCode0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural NetworksCode0
BoXHED2.0: Scalable boosting of dynamic survival analysisCode0
Feature Selection for Survival Analysis with Competing Risks using Deep LearningCode0
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features RelateCode0
Neurological Prognostication of Post-Cardiac-Arrest Coma Patients Using EEG Data: A Dynamic Survival Analysis Framework with Competing RisksCode0
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