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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
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
A Deep Variational Approach to Clustering Survival DataCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Deep Cox Mixtures for Survival RegressionCode1
X-CAL: Explicit Calibration for Survival AnalysisCode1
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue SystemsCode1
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation NetworksCode1
DeepHazard: neural network for time-varying risksCode1
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
Enabling Counterfactual Survival Analysis with Balanced RepresentationsCode1
A General Framework for Survival Analysis and Multi-State ModellingCode1
A Deep Recurrent Survival Model for Unbiased RankingCode1
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Survival Cluster AnalysisCode1
Adversarial Time-to-Event ModelingCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies—0
Soft decision trees for survival analysis—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
Survival Analysis as Imprecise Classification with Trainable KernelsCode0
Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach—0
Distributionally Robust Learning in Survival Analysis—0
Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning—0
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