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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 201–250 of 472 papers

TitleStatusHype
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models—0
Robust Survival Analysis with Adversarial Regularization—0
Scaling Survival Analysis in Healthcare with Federated Survival Forests: A Comparative Study on Heart Failure and Breast Cancer Genomics—0
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance—0
Searching for the "Holy Grail" of sponsorship-linked marketing: A generalizable sponsorship ROI model—0
Secure and Differentially Private Bayesian Learning on Distributed Data—0
Semi-Structured Deep Piecewise Exponential Models—0
SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data—0
SGD with Variance Reduction beyond Empirical Risk Minimization—0
Shared Hardships Strengthen Bonds: Negative Shocks, Embeddedness and Employee Retention—0
Siamese Survival Analysis with Competing Risks—0
Simpler Calibration for Survival Analysis—0
Soft decision trees for survival analysis—0
Statistical Inference for Data-adaptive Doubly Robust Estimators with Survival Outcomes—0
Statistics of punctuation in experimental literature -- the remarkable case of "Finnegans Wake" by James Joyce—0
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis—0
Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data—0
Survival Modeling of Suicide Risk with Rare and Uncertain Diagnoses—0
Kernel Machines for Current Status Data—0
survAIval: Survival Analysis with the Eyes of AI—0
SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation—0
SurvCORN: Survival Analysis with Conditional Ordinal Ranking Neural Network—0
Survival Analysis of Young Triple-Negative Breast Cancer Patients—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Survival Analysis Revisited: Understanding and Unifying Poisson, Exponential, and Cox Models in Fall Risk Analysis—0
Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment—0
Survival and Neural Models for Private Equity Exit Prediction—0
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission—0
Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
Survival-oriented embeddings for improving accessibility to complex data structures—0
Survival Seq2Seq: A Survival Model based on Sequence to Sequence Architecture—0
Survival-Supervised Topic Modeling with Anchor Words: Characterizing Pancreatitis Outcomes—0
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models—0
SurvNAM: The machine learning survival model explanation—0
Interpretable ML for High-Frequency Execution—0
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift—0
Targeting Learning: Robust Statistics for Reproducible Research—0
Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks—0
Temporal Pattern Mining for Analysis of Longitudinal Clinical Data: Identifying Risk Factors for Alzheimer's Disease—0
The Expediting Effect of Monitoring on Infrastructural Works. A Regression-Discontinuity Approach with Multiple Assignment Variables—0
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence—0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data—0
Time-to-Event Prediction with Neural Networks and Cox Regression—0
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits—0
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches—0
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning—0
Towards inferring network properties from epidemic data—0
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation—0
Towards simple time-to-event modeling: optimizing neural networks via rank regression—0
On Training Survival Models with Scoring Rules—0
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