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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 26–50 of 472 papers

TitleStatusHype
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival PredictionCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Cross-Modal Translation and Alignment for Survival AnalysisCode1
DeepHazard: neural network for time-varying risksCode1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
CenTime: Event-Conditional Modelling of Censoring in Survival AnalysisCode1
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
A Comparative Study for Time-to-Event Analysis and Survival Prediction for Heart Failure Condition using Machine Learning TechniquesCode1
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability GuaranteesCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing RisksCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
A Deep Recurrent Survival Model for Unbiased RankingCode1
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
HEALNet: Multimodal Fusion for Heterogeneous Biomedical DataCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
An Introduction to Deep Survival Analysis Models for Predicting Time-to-Event OutcomesCode1
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
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