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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 311–320 of 472 papers

TitleStatusHype
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values—0
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients—0
Better Approximate Inference for Partial Likelihood Models with a Latent Structure—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis—0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Causal Inference for Survival Analysis—0
Child Care Provider Survival Analysis—0
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