SOTAVerified

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

Showing 4150 of 472 papers

TitleStatusHype
Generalized Bayesian Ensemble Survival Tree (GBEST) model0
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event DataCode0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records0
Adaptive Prototype Learning for Multimodal Cancer Survival AnalysisCode0
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and RetentionCode1
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis0
Overcoming Dependent Censoring in the Evaluation of Survival ModelsCode0
Enhancing External Validity of Experiments with Ongoing Sampling0
Censor Dependent Variational InferenceCode0
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis0
Show:102550
← PrevPage 5 of 48Next →

No leaderboard results yet.