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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 126–150 of 472 papers

TitleStatusHype
Region-specific Risk Quantification for Interpretable Prognosis of COVID-19Code0
A probabilistic estimation of remaining useful life from censored time-to-event dataCode1
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)Code0
Estimation of Time-to-Total Knee Replacement Surgery—0
The TruEnd-procedure: Treating trailing zero-valued balances in credit data—0
Interpretable Prediction and Feature Selection for Survival Analysis—0
Explainable Survival Analysis with Uncertainty using Convolution-Involved Vision Transformer—0
TorchSurv: A Lightweight Package for Deep Survival AnalysisCode2
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis—0
Efficient Training of Probabilistic Neural Networks for Survival AnalysisCode0
Analyzing Economic Convergence Across the Americas: A Survival Analysis Approach to GDP per Capita Trajectories—0
Cohort-Individual Cooperative Learning for Multimodal Cancer Survival AnalysisCode1
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric CancerCode1
Shared Hardships Strengthen Bonds: Negative Shocks, Embeddedness and Employee Retention—0
On Training Survival Models with Scoring Rules—0
Dynamic Survival Analysis for Early Event Prediction—0
Interpretable Machine Learning for Survival AnalysisCode0
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context InteractionCode2
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival DataCode0
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission—0
A network-constrain Weibull AFT model for biomarkers discovery—0
Differentially Private Distributed InferenceCode0
Online Learning Approach for Survival Analysis—0
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis—0
Explainable AI for survival analysis: a median-SHAP approach—0
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