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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 Cluster AnalysisCode1
DeepHazard: neural network for time-varying risksCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer PrognosisCode1
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual RepresentationCode1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
SurvHive: a package to consistently access multiple survival-analysis packagesCode1
A kernel log-rank test of independence for right-censored dataCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Learning Survival Distribution with Implicit Survival FunctionCode0
Adaptive Transformer Modelling of Density Function for Nonparametric Survival AnalysisCode0
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
A survey of Transformer applications for histopathological image analysis: New developments and future directionsCode0
A Study on Survival Analysis Methods Using Neural Network to Prevent CancersCode0
A General Machine Learning Framework for Survival AnalysisCode0
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