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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 101–150 of 472 papers

TitleStatusHype
Fairness in Survival Analysis with Distributionally Robust OptimizationCode0
Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce—0
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
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological ImagesCode1
A Closer Look at Mortality Risk Prediction from ElectrocardiogramsCode1
Knowledge-driven Subspace Fusion and Gradient Coordination for Multi-modal LearningCode0
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
MoME: Mixture of Multimodal Experts for Cancer Survival PredictionCode1
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
Harnessing the power of longitudinal medical imaging for eye disease prognosis using Transformer-based sequence modelingCode1
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
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
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
TorchSurv: A Lightweight Package for Deep Survival AnalysisCode2
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
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric CancerCode1
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
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
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
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