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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 351–400 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
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model—0
Federated Survival Analysis with Discrete-Time Cox Models—0
Targeting Learning: Robust Statistics for Reproducible Research—0
Secure and Differentially Private Bayesian Learning on Distributed Data—0
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models—0
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical TrialsCode0
An RNN-Survival Model to Decide Email Send Times—0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours—0
Variational Learning of Individual Survival DistributionsCode0
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images—0
Estimation of conditional mixture Weibull distribution with right-censored data using neural network for time-to-event analysis—0
Uncovering life-course patterns with causal discovery and survival analysis—0
Multi-Task Deep Learning: Simultaneous Segmentation and Survival Analysis via Cox Proportional Hazards Regression—0
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group—0
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion ProcessesCode0
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches—0
The Brier Score under Administrative Censoring: Problems and SolutionsCode0
A kernel log-rank test of independence for right-censored dataCode0
Survival and Neural Models for Private Equity Exit Prediction—0
Better Approximate Inference for Partial Likelihood Models with a Latent Structure—0
Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival AnalysisCode0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
Variable Selection with Random Survival Forest and Bayesian Additive Regression Tree for Survival Data—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
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