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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

Showing 111120 of 472 papers

TitleStatusHype
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression AnalysisCode0
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological ImagesCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal LearningCode0
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks0
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba0
Conditioning on Time is All You Need for Synthetic Survival Data GenerationCode0
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