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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 426450 of 472 papers

TitleStatusHype
Copula Entropy based Variable Selection for Survival AnalysisCode0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)Code0
Self-Consistent Equation-guided Neural Networks for Censored Time-to-Event DataCode0
Contextual Explanation NetworksCode0
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive ModellingCode0
Context Matters: Query-aware Dynamic Long Sequence Modeling of Gigapixel ImagesCode0
VGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic ReconstructionCode0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
A Study on Survival Analysis Methods Using Neural Network to Prevent CancersCode0
ICTSurF: Implicit Continuous-Time Survival Functions with Neural NetworksCode0
Using Geographic Location-based Public Health Features in Survival AnalysisCode0
The structure of online social networks modulates the rate of lexical changeCode0
Conformalized Survival Distributions: A Generic Post-Process to Increase CalibrationCode0
Learning Survival Distribution with Implicit Survival FunctionCode0
Overcoming Dependent Censoring in the Evaluation of Survival ModelsCode0
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modellingCode0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Integrated Machine Learning and Survival Analysis Modeling for Enhanced Chronic Kidney Disease Risk StratificationCode0
Simultaneous Prediction Intervals for Patient-Specific Survival CurvesCode0
A Scalable Discrete-Time Survival Model for Neural NetworksCode0
Interpretable Machine Learning for Survival AnalysisCode0
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic RegressionCode0
Variational Learning of Individual Survival DistributionsCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
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