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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

Showing 2650 of 472 papers

TitleStatusHype
Optimal Survival Trees: A Dynamic Programming ApproachCode1
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological ImagesCode1
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability GuaranteesCode1
HEALNet: Multimodal Fusion for Heterogeneous Biomedical DataCode1
Sensitivity of Survival Analysis MetricsCode1
Cross-Modal Translation and Alignment for Survival AnalysisCode1
CenTime: Event-Conditional Modelling of Censoring in Survival AnalysisCode1
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival PredictionCode1
Deep Learning for Survival Analysis: A ReviewCode1
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
SurvivalGAN: Generating Time-to-Event Data for Survival AnalysisCode1
SurvLIMEpy: A Python package implementing SurvLIMECode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Survival Mixture Density NetworksCode1
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
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 Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical DataCode1
Survival Analysis Algorithms based on Decision Trees with Weighted Log-rank CriteriaCode1
SurvTRACE: Transformers for Survival Analysis with Competing EventsCode1
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