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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 276–300 of 472 papers

TitleStatusHype
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Deep conditional transformation models for survival analysis—0
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker—0
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Factor-Augmented Regularized Model for Hazard Regression—0
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease—0
Copula Entropy based Variable Selection for Survival AnalysisCode0
Temporal Label Smoothing for Early Event PredictionCode0
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review—0
Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning—0
Child Care Provider Survival Analysis—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines—0
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data—0
Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis—0
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy—0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns—0
User Engagement in Mobile Health Applications—0
A Multiple kernel testing procedure for non-proportional hazards in factorial designs—0
No-regret Learning in Repeated First-Price Auctions with Budget Constraints—0
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