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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 351–375 of 472 papers

TitleStatusHype
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting—0
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values—0
Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer—0
Research Reproducibility as a Survival AnalysisCode0
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach—0
Positive-Unlabelled Survival Data Analysis—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Using ontology embeddings for structural inductive bias in gene expression data analysis—0
Semi-Structured Deep Piecewise Exponential Models—0
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis—0
Programmed death ligand 1 expression levels, clinicopathologic features, and survival in surgically resected sarcomatoid lung carcinoma—0
Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses—0
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging—0
Kernelized Stein Discrepancy Tests of Goodness-of-fit for Time-to-Event Data—0
mlr3proba: An R Package for Machine Learning in Survival Analysis—0
Probability Link Models with Symmetric Information Divergence—0
Predicting risk of late age-related macular degeneration using deep learning—0
Estimating and interpreting secondary attack risk: Binomial considered harmful—0
Neural Topic Models with Survival Supervision: Jointly Predicting Time-to-Event Outcomes and Learning How Clinical Features RelateCode0
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival AnalysisCode0
Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis—0
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
Predicting Customer Churn in World of Warcraft—0
A General Machine Learning Framework for Survival AnalysisCode0
BoXHED: Boosted eXact Hazard Estimator with Dynamic covariatesCode0
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