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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 111–120 of 472 papers

TitleStatusHype
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis—0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach—0
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel ImagesCode0
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift—0
A Multi-Modal Deep Learning Framework for Pan-Cancer PrognosisCode0
Improved joint modelling of breast cancer radiomics features and hazard by image registration aided longitudinal CT data—0
Survival Analysis Revisited: Understanding and Unifying Poisson, Exponential, and Cox Models in Fall Risk Analysis—0
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis—0
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation—0
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