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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 151–160 of 472 papers

TitleStatusHype
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis—0
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction—0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Dynamic prediction of time to event with survival curves—0
Dynamic Survival Analysis for Early Event Prediction—0
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
DySurv: dynamic deep learning model for survival analysis with conditional variational inference—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Discrete Stochastic Models in Continuous Time for Ecology—0
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