SOTAVerified

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
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual RepresentationCode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing RisksCode1
Sensitivity of Survival Analysis MetricsCode1
SurvHive: a package to consistently access multiple survival-analysis packagesCode1
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
Show:102550
← PrevPage 6 of 48Next →

No leaderboard results yet.