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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 126–150 of 472 papers

TitleStatusHype
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—0
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia—0
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
A Cost-Aware Approach to Adversarial Robustness in Neural Networks—0
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles—0
Child Care Provider Survival Analysis—0
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories—0
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis—0
Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach—0
A Multiple kernel testing procedure for non-proportional hazards in factorial designs—0
A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks—0
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models—0
Estimation of Time-to-Total Knee Replacement Surgery—0
Explainable AI for survival analysis: a median-SHAP approach—0
Causal Inference for Survival Analysis—0
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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