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Survival Analysis
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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.
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Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies
Soft decision trees for survival analysis
Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence
Survival Analysis as Imprecise Classification with Trainable Kernels
Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach
Distributionally Robust Learning in Survival Analysis
Stop Chasing the C-index: This Is How We Should Evaluate Our Survival Models
A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis
Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planning
CardioCoT: Hierarchical Reasoning for Multimodal Survival Analysis
Will Large Language Models Transform Clinical Prediction?
Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare
SurvUnc: A Meta-Model Based Uncertainty Quantification Framework for Survival Analysis
Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data
Multimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification
Too Sick for Working, or Sick of Working? Impact of Acute Health Shocks on Early Labour Market Exits
STG: Spatiotemporal Graph Neural Network with Fusion and Spatiotemporal Decoupling Learning for Prognostic Prediction of Colorectal Cancer Liver Metastasis
Learning Survival Distributions with the Asymmetric Laplace Distribution
Prediction of Delirium Risk in Mild Cognitive Impairment Using Time-Series data, Machine Learning and Comorbidity Patterns -- A Retrospective Study
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis
Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation
Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications
Offline Dynamic Inventory and Pricing Strategy: Addressing Censored and Dependent Demand
Forecasting from Clinical Textual Time Series: Adaptations of the Encoder and Decoder Language Model Families
Predicting the Lifespan of Industrial Printheads with Survival Analysis
ICBM community cancer registry analysis: a focus on Non-Hodgkin Lymphoma cases in missileers
Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression
Vision Transformers with Autoencoders and Explainable AI for Cancer Patient Risk Stratification Using Whole Slide Imaging
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI
Extending Cox Proportional Hazards Model with Symbolic Non-Linear Log-Risk Functions for Survival Analysis
Improving Diseases Predictions Utilizing External Bio-Banks
AdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival Prediction
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