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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 226–250 of 472 papers

TitleStatusHype
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
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