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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 141–150 of 472 papers

TitleStatusHype
A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis—0
A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis—0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis—0
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction—0
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting—0
Enhancing External Validity of Experiments with Ongoing Sampling—0
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare—0
AMMASurv: Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data—0
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis—0
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