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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 201–250 of 472 papers

TitleStatusHype
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers—0
Deep Learning for Survival Analysis: A ReviewCode1
Learning Survival Distribution with Implicit Survival FunctionCode0
survAIval: Survival Analysis with the Eyes of AI—0
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular InformationCode0
Neural Fine-Gray: Monotonic neural networks for competing risksCode0
Proper Scoring Rules for Survival AnalysisCode0
Diffsurv: Differentiable sorting for censored time-to-event dataCode0
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions—0
Using Geographic Location-based Public Health Features in Survival AnalysisCode0
Searching for the "Holy Grail" of sponsorship-linked marketing: A generalizable sponsorship ROI model—0
Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes—0
CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks—0
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study—0
Interpretable machine learning for time-to-event prediction in medicine and healthcareCode1
Continuous Risk Measures for Driving Support—0
Probabilistic Uncertainty-Aware Risk Spot Detector for Naturalistic Driving—0
Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps—0
Optimization of Velocity Ramps with Survival Analysis for Intersection Merge-Ins—0
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and CountingCode1
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients—0
Discrete-time Competing-Risks Regression with or without PenalizationCode1
SurvivalGAN: Generating Time-to-Event Data for Survival AnalysisCode1
A Statistical Learning Take on the Concordance Index for Survival Analysis—0
SurvLIMEpy: A Python package implementing SurvLIMECode1
Federated Survival ForestsCode0
Towards inferring network properties from epidemic data—0
The Past, Current, and Future of Neonatal Intensive Care Units with Artificial Intelligence—0
Heterogeneous Datasets for Federated Survival Analysis SimulationCode0
Adaptive Sampling for Weighted Log-Rank Survival Trees BoostingCode1
Deep Clustering Survival Machines with Interpretable Expert DistributionsCode0
Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide ImagesCode0
Case-Base Neural Networks: survival analysis with time-varying, higher-order interactionsCode0
Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identification—0
Hospital transfer risk prediction for COVID-19 patients from a medicalized hotel based on Diffusion GraphSAGE—0
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithm—0
AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide ImagesCode1
Examining marginal properness in the external validation of survival models with squared and logarithmic lossesCode0
Multi-Source Survival Domain AdaptationCode0
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsCode0
Variable selection for nonlinear Cox regression model via deep learning—0
Extending the Neural Additive Model for Survival Analysis with EHR Data—0
Actionable Recourse via GANs for Mobile Health—0
Multimodal Learning for Non-small Cell Lung Cancer Prognosis—0
A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation—0
Reverse Survival Model (RSM): A Pipeline for Explaining Predictions of Deep Survival Models—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Predicting Survival Outcomes in the Presence of Unlabeled Data—0
Deep conditional transformation models for survival analysis—0
Integrative Pan-Cancer Analysis of RNMT: a Potential Prognostic and Immunological Biomarker—0
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