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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 151–200 of 472 papers

TitleStatusHype
MENSA: A Multi-Event Network for Survival Analysis under Informative CensoringCode0
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images—0
Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator—0
Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce—0
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive ModellingCode0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations—0
Forecasting Automotive Supply Chain Shortfalls with Heterogeneous Time Series—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
SurvReLU: Inherently Interpretable Survival Analysis via Deep ReLU NetworksCode0
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
TE-SSL: Time and Event-aware Self Supervised Learning for Alzheimer's Disease Progression AnalysisCode0
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal LearningCode0
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes—0
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional DataCode0
Modeling Long Sequences in Bladder Cancer Recurrence: A Comparative Evaluation of LSTM,Transformer,and Mamba—0
Conditioning on Time is All You Need for Synthetic Survival Data GenerationCode0
Clustering Survival Data using a Mixture of Non-parametric ExpertsCode0
Comparing ImageNet Pre-training with Digital Pathology Foundation Models for Whole Slide Image-Based Survival Analysis—0
Conformalized Survival Distributions: A Generic Post-Process to Increase CalibrationCode0
ResSurv: Cancer Survival Analysis Prediction Model Based on Residual Networks—0
Region-specific Risk Quantification for Interpretable Prognosis of COVID-19Code0
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
Estimation of Time-to-Total Knee Replacement Surgery—0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data—0
Interpretable Prediction and Feature Selection for Survival Analysis—0
Explainable Survival Analysis with Uncertainty using Convolution-Involved Vision Transformer—0
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories—0
Shared Hardships Strengthen Bonds: Negative Shocks, Embeddedness and Employee Retention—0
On Training Survival Models with Scoring Rules—0
Dynamic Survival Analysis for Early Event Prediction—0
Interpretable Machine Learning for Survival AnalysisCode0
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission—0
A network-constrain Weibull AFT model for biomarkers discovery—0
Differentially Private Distributed InferenceCode0
Online Learning Approach for Survival Analysis—0
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis—0
Explainable AI for survival analysis: a median-SHAP approach—0
Dynamical Survival Analysis with Controlled Latent StatesCode0
High-Dimensional False Discovery Rate Control for Dependent Variables—0
Optimal Sparse Survival TreesCode0
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance—0
Survival Analysis of Young Triple-Negative Breast Cancer Patients—0
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival AnalysisCode0
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