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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 51–100 of 472 papers

TitleStatusHype
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
Adversarial Time-to-Event ModelingCode1
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Survival Analysis Algorithms based on Decision Trees with Weighted Log-rank CriteriaCode1
Discrete-time Competing-Risks Regression with or without PenalizationCode1
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural NetworkCode1
DeepHazard: neural network for time-varying risksCode1
CDS -- Causal Inference with Deep Survival Model and Time-varying CovariatesCode1
Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological ImagesCode1
CustOmics: A versatile deep-learning based strategy for multi-omics integrationCode1
MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and RetentionCode1
Cross-Modal Translation and Alignment for Survival AnalysisCode1
Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal CancerCode1
Hierarchical Bayesian Modelling for Knowledge Transfer Across Engineering Fleets via Multitask LearningCode1
PyDTS: A Python Package for Discrete-Time Survival (Regularized) Regression with Competing RisksCode1
Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression LabelsCode1
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological ImagesCode1
A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data—0
A Versatile Influence Function for Data Attribution with Non-Decomposable Loss—0
4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis—0
A unified construction for series representations and finite approximations of completely random measures—0
Attention-Based Synthetic Data Generation for Calibration-Enhanced Survival Analysis: A Case Study for Chronic Kidney Disease Using Electronic Health Records—0
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study—0
Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction—0
Deep survival analysis with longitudinal X-rays for COVID-19—0
Deep Survival Analysis—0
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
A State Transition Model for Mobile Notifications via Survival Analysis—0
Development of digitally obtainable 10-year risk scores for depression and anxiety in the general population—0
Assumption-Free Survival Analysis Under Local Smoothness Prior—0
DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis—0
DeepWait: Pedestrian Wait Time Estimation in Mixed Traffic Conditions Using Deep Survival Analysis—0
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis—0
An RNN-Survival Model to Decide Email Send Times—0
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care—0
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis—0
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm—0
DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging—0
Differentially Private Regression for Discrete-Time Survival Analysis—0
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores—0
Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework—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
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR—0
Combining multi-site Magnetic Resonance Imaging with machine learning predicts survival in paediatric brain tumours—0
Unsupervised Machine Learning for the Discovery of Latent Disease Clusters and Patient Subgroups Using Electronic Health Records—0
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery—0
CNN-based Survival Model for Pancreatic Ductal Adenocarcinoma in Medical Imaging—0
A network-constrain Weibull AFT model for biomarkers discovery—0
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