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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

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Showing 121–130 of 472 papers

TitleStatusHype
Clustering Survival Data using a Mixture of Non-parametric ExpertsCode0
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis—0
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
Conformalized Survival Distributions: A Generic Post-Process to Increase CalibrationCode0
ResSurv: Cancer Survival Analysis Prediction Model Based on Residual Networks—0
Region-specific Risk Quantification for Interpretable Prognosis of COVID-19Code0
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)Code0
Estimation of Time-to-Total Knee Replacement Surgery—0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data—0
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