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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 271–280 of 472 papers

TitleStatusHype
Wavelet feature extraction and genetic algorithm for biomarker detection in colorectal cancer data—0
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy—0
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis—0
Will Large Language Models Transform Clinical Prediction?—0
WSISA: Making Survival Prediction From Whole Slide Histopathological Images—0
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis—0
Your Actions or Your Associates? Predicting Certification and Dropout in MOOCs with Behavioral and Social Features—0
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records—0
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification—0
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models—0
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