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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 326–350 of 472 papers

TitleStatusHype
Metaparametric Neural Networks for Survival Analysis—0
Energy-based survival modelling using harmoniumsCode0
Simpler Calibration for Survival Analysis—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
Towards simple time-to-event modeling: optimizing neural networks via rank regression—0
Early ICU Mortality Prediction and Survival Analysis for Respiratory Failure—0
AMMASurv: Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data—0
Deep survival analysis with longitudinal X-rays for COVID-19—0
ALBRT: Cellular Composition Prediction in Routine Histology ImagesCode0
Individual Survival Curves with Conditional Normalizing Flows—0
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis—0
Mortality Analysis of Early COVID-19 Cases in the Philippines Based on Observed Demographic and Clinical Characteristics—0
Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient MortalityCode0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
SurvNAM: The machine learning survival model explanation—0
The structure of online social networks modulates the rate of lexical changeCode0
BoXHED2.0: Scalable boosting of dynamic survival analysisCode0
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients—0
Conformalized Survival AnalysisCode0
Predicting Kidney Transplant Survival using Multiple Feature Representations for HLAs—0
Exploring the Wasserstein metric for survival analysisCode0
The Expediting Effect of Monitoring on Infrastructural Works. A Regression-Discontinuity Approach with Multiple Assignment Variables—0
Variational Bayes survival analysis for unemployment modelling—0
Computing the Hazard Ratios Associated with Explanatory Variables Using Machine Learning Models of Survival DataCode0
Dynamic prediction of time to event with survival curves—0
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