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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 5160 of 472 papers

TitleStatusHype
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
Adversarial Time-to-Event ModelingCode1
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation NetworksCode1
CenTime: Event-Conditional Modelling of Censoring in Survival AnalysisCode1
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
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