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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 121130 of 472 papers

TitleStatusHype
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
Censor Dependent Variational InferenceCode0
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival AnalysisCode0
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic informationCode0
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive ModellingCode0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
Dynamical Survival Analysis with Controlled Latent StatesCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
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