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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 125 of 472 papers

TitleStatusHype
FastCPH: Efficient Survival Analysis for Neural NetworksCode2
TorchSurv: A Lightweight Package for Deep Survival AnalysisCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
forester: A Tree-Based AutoML Tool in RCode2
Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational PathologyCode2
Neural interval-censored survival regression with feature selectionCode2
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction IntervalsCode1
Deep Cox Mixtures for Survival RegressionCode1
Deep Learning for Survival Analysis: A ReviewCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability GuaranteesCode1
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
DeepHazard: neural network for time-varying risksCode1
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival AnalysisCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Censored Quantile Regression Neural Networks for Distribution-Free Survival AnalysisCode1
CenTime: Event-Conditional Modelling of Censoring in Survival AnalysisCode1
A Deep Recurrent Survival Model for Unbiased RankingCode1
A Deep Variational Approach to Clustering Survival DataCode1
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
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival AnalysisCode1
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