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 351360 of 472 papers

TitleStatusHype
Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses0
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging0
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation NetworksCode1
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data0
mlr3proba: An R Package for Machine Learning in Survival Analysis0
Probability Link Models with Symmetric Information Divergence0
DeepHazard: neural network for time-varying risksCode1
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
Predicting risk of late age-related macular degeneration using deep learning0
Estimating and interpreting secondary attack risk: Binomial considered harmful0
Show:102550
← PrevPage 36 of 48Next →

No leaderboard results yet.