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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
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
SODEN: A Scalable Continuous-Time Survival Model through Ordinary Differential Equation NetworksCode1
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
DeepHazard: neural network for time-varying risksCode1
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
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
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
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
Enabling Counterfactual Survival Analysis with Balanced RepresentationsCode1
Targeting Learning: Robust Statistics for Reproducible Research—0
A General Framework for Survival Analysis and Multi-State ModellingCode1
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
A Deep Recurrent Survival Model for Unbiased RankingCode1
Survival Analysis Using a 5-Step Stratified Testing and Amalgamation Routine in Randomized Clinical TrialsCode0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours—0
An RNN-Survival Model to Decide Email Send Times—0
Variational Learning of Individual Survival DistributionsCode0
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images—0
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Survival Cluster AnalysisCode1
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
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
Modeling Time to Open of Emails with a Latent State for User Engagement Level—0
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