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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 251–300 of 472 papers

TitleStatusHype
EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological ImagesCode0
Factor-Augmented Regularized Model for Hazard Regression—0
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease—0
Copula Entropy based Variable Selection for Survival AnalysisCode0
Temporal Label Smoothing for Early Event PredictionCode0
Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review—0
Survival Mixture Density NetworksCode1
FastCPH: Efficient Survival Analysis for Neural NetworksCode2
Necessary and sufficient conditions for exact closures of epidemic equations on configuration model networks—0
KL-divergence Based Deep Learning for Discrete Time Model—0
Child Care Provider Survival Analysis—0
Multi-Scale User Behavior Network for Entire Space Multi-Task Learning—0
Likelihood-Free Dynamical Survival Analysis Applied to the COVID-19 Epidemic in Ohio—0
Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines—0
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data—0
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
FedPseudo: Pseudo value-based Deep Learning Models for Federated Survival Analysis—0
Pseudo value-based Deep Neural Networks for Multi-state Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy—0
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeCode0
Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns—0
User Engagement in Mobile Health Applications—0
A Multiple kernel testing procedure for non-proportional hazards in factorial designs—0
Neural interval-censored survival regression with feature selectionCode2
No-regret Learning in Repeated First-Price Auctions with Budget Constraints—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Hazard Gradient Penalty for Survival Analysis—0
Flexible Group Fairness Metrics for Survival AnalysisCode0
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
Predicting Time-to-conversion for Dementia of Alzheimer's Type using Multi-modal Deep Survival Analysis—0
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing RisksCode1
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture—0
Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks—0
Calibration Error for Heterogeneous Treatment EffectsCode0
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical DataCode1
SimHawNet: A Modified Hawkes Process for Temporal Network SimulationCode0
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction modelsCode0
Finite-Sum Coupled Compositional Stochastic Optimization: Theory and Applications—0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
Generalized Bayesian Additive Regression Trees Models: Beyond Conditional Conjugacy—0
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis—0
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data—0
Survival Analysis Algorithms based on Decision Trees with Weighted Log-rank CriteriaCode1
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival RiskCode0
A Multi-modal Fusion Framework Based on Multi-task Correlation Learning for Cancer Prognosis Prediction—0
Pricing Time-to-Event Contingent Cash Flows: A Discrete-Time Survival Analysis Approach—0
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measuresCode0
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