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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 351–375 of 472 papers

TitleStatusHype
Survival Analysis as Imprecise Classification with Trainable KernelsCode0
Deep Learning for Patient-Specific Kidney Graft Survival AnalysisCode0
MENSA: A Multi-Event Network for Survival Analysis under Informative CensoringCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
Deep Landscape Forecasting for Real-time Bidding AdvertisingCode0
DNAMite: Interpretable Calibrated Survival Analysis with Discretized Additive ModelsCode0
DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo ValuesCode0
Doubly Robust Conformalized Survival Analysis with Right-Censored DataCode0
Dynamical Survival Analysis with Controlled Latent StatesCode0
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation LearningCode0
An Efficient Training Algorithm for Kernel Survival Support Vector MachinesCode0
Clustering Survival Data using a Mixture of Non-parametric ExpertsCode0
Dynamic Survival Analysis for non-Markovian Epidemic ModelsCode0
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health recordsCode0
Proper Scoring Rules for Survival AnalysisCode0
A Multi-Modal Deep Learning Framework for Pan-Cancer PrognosisCode0
A survey of Transformer applications for histopathological image analysis: New developments and future directionsCode0
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival PredictionCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
Temporal Label Smoothing for Early Event PredictionCode0
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival RiskCode0
Censor Dependent Variational InferenceCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
DeepHit: A Deep Learning Approach to Survival Analysis with Competing RisksCode0
Towards Flexible Time-to-event Modeling: Optimizing Neural Networks via Rank RegressionCode0
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