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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 51–75 of 472 papers

TitleStatusHype
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
Adversarial Time-to-Event ModelingCode1
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Survival Analysis with Machine Learning for Predicting Li-ion Battery Remaining Useful LifeCode1
Cross-Modal Translation and Alignment for Survival AnalysisCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
SurvTRACE: Transformers for Survival Analysis with Competing EventsCode1
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological ImagesCode1
X-CAL: Explicit Calibration for Survival AnalysisCode1
Deep Cox Mixtures for Survival RegressionCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
Sensitivity of Survival Analysis MetricsCode1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
DeepHazard: neural network for time-varying risksCode1
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis—0
A unified construction for series representations and finite approximations of completely random measures—0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records—0
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study—0
Copula-Based Deep Survival Models for Dependent Censoring—0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
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