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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
An Approach for Clustering Subjects According to Similarities in Cell Distributions within Biopsies—0
A network-constrain Weibull AFT model for biomarkers discovery—0
An evaluation of machine learning techniques to predict the outcome of children treated for Hodgkin-Lymphoma on the AHOD0031 trial: A report from the Children's Oncology Group—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
An RNN-Survival Model to Decide Email Send Times—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records—0
A unified construction for series representations and finite approximations of completely random measures—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
BDNNSurv: Bayesian deep neural networks for survival analysis using pseudo values—0
BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients—0
Better Approximate Inference for Partial Likelihood Models with a Latent Structure—0
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs—0
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis—0
CARMIL: Context-Aware Regularization on Multiple Instance Learning models for Whole Slide Images—0
Causal Inference for Survival Analysis—0
Child Care Provider Survival Analysis—0
Churn Prediction in Mobile Social Games: Towards a Complete Assessment Using Survival Ensembles—0
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours—0
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Continuous and Discrete-Time Survival Prediction with Neural Networks—0
Continuous Risk Measures for Driving Support—0
Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records—0
Copula-Based Deep Survival Models for Dependent Censoring—0
CoxSE: Exploring the Potential of Self-Explaining Neural Networks with Cox Proportional Hazards Model for Survival Analysis—0
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers—0
Deep conditional transformation models for survival analysis—0
Deep Convolutional Neural Networks for Imaging Data Based Survival Analysis of Rectal Cancer—0
Deep End-to-End Survival Analysis with Temporal Consistency—0
Deep Extended Hazard Models for Survival Analysis—0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm—0
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis—0
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis—0
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging—0
Deep Survival Analysis—0
Deep survival analysis with longitudinal X-rays for COVID-19—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Discrete Stochastic Models in Continuous Time for Ecology—0
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