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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 301–350 of 472 papers

TitleStatusHype
Deep Extended Hazard Models for Survival Analysis—0
Inverse-Weighted Survival GamesCode0
DAGSurv: Directed Acyclic Graph Based Survival Analysis Using Deep Neural NetworksCode0
Predictive factors associated with survival rate of cervical cancer patients in Brunei Darussalam—0
Survival-oriented embeddings for improving accessibility to complex data structures—0
Towards modelling hazard factors in unstructured data spaces using gradient-based latent interpolation—0
Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards—0
Metaparametric Neural Networks for Survival Analysis—0
Energy-based survival modelling using harmoniumsCode0
SurvTRACE: Transformers for Survival Analysis with Competing EventsCode1
Towards simple time-to-event modeling: optimizing neural networks via rank regression—0
Simpler Calibration for Survival Analysis—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—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
Locally Sparse Neural Networks for Tabular Biomedical DataCode1
A Deep Variational Approach to Clustering Survival DataCode1
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
EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting—0
Dynamic prediction of time to event with survival curves—0
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Deep Cox Mixtures for Survival RegressionCode1
X-CAL: Explicit Calibration for Survival AnalysisCode1
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values—0
Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer—0
Research Reproducibility as a Survival AnalysisCode0
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach—0
Positive-Unlabelled Survival Data Analysis—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Using ontology embeddings for structural inductive bias in gene expression data analysis—0
Long-Term Pipeline Failure Prediction Using Nonparametric Survival Analysis—0
Semi-Structured Deep Piecewise Exponential Models—0
Programmed death ligand 1 expression levels, clinicopathologic features, and survival in surgically resected sarcomatoid lung carcinoma—0
Spot The Bot: A Robust and Efficient Framework for the Evaluation of Conversational Dialogue SystemsCode1
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