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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 101110 of 472 papers

TitleStatusHype
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce0
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive ModellingCode0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images0
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations0
Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models0
SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU NetworksCode0
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review0
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