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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 151–175 of 472 papers

TitleStatusHype
Dynamical Survival Analysis with Controlled Latent StatesCode0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
Optimal Sparse Survival TreesCode0
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance—0
Survival Analysis of Young Triple-Negative Breast Cancer Patients—0
Optimal Survival Trees: A Dynamic Programming ApproachCode1
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival AnalysisCode0
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological ImagesCode1
Robust Survival Analysis with Adversarial Regularization—0
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability GuaranteesCode1
SAVAE: Leveraging the variational Bayes autoencoder for survival analysisCode0
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health recordsCode0
ICTSurF: Implicit Continuous-Time Survival Functions with Neural NetworksCode0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics DataCode0
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic informationCode0
SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/RecordsCode0
HEALNet: Multimodal Fusion for Heterogeneous Biomedical DataCode1
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia—0
Likelihood Ratio Confidence Sets for Sequential Decision Making—0
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling—0
Higher Mediterranean diet score is associated with longer time between relapses in Australian females with multiple sclerosis—0
DySurv: dynamic deep learning model for survival analysis with conditional variational inference—0
Improving Event Time Prediction by Learning to Partition the Event Time Space—0
Interpretable Survival Analysis for Heart Failure Risk Prediction—0
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