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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
Distributionally Robust Learning in Survival Analysis—0
Dynamic prediction of time to event with survival curves—0
Dynamic Survival Analysis for Early Event Prediction—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
DySurv: dynamic deep learning model for survival analysis with conditional variational inference—0
Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure—0
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes—0
End-Stage Liver Disease Comorbidities in Patients Awaiting Transplantation: Identification and Impact on Liver Transplant Survival—0
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images—0
Enhanced Lung Cancer Survival Prediction using Semi-Supervised Pseudo-Labeling and Learning from Diverse PET/CT Datasets—0
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