Survival Analysis
133 papers with code • 0 benchmarks • 4 datasets
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.
Benchmarks
These leaderboards are used to track progress in Survival Analysis
Libraries
Use these libraries to find Survival Analysis models and implementationsDatasets
GBSG2
Latest papers
SAVAE: Leveraging the variational Bayes autoencoder for survival analysis
patricia-a-apellaniz/savae
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As in many fields of medical research, survival analysis has witnessed a growing interest in the application of deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored medical data.
MixEHR-SurG: a joint proportional hazard and guided topic model for inferring mortality-associated topics from electronic health records
This leads to a highly interpretable survival topic model that can infer PheCode-specific phenotype topics associated with patient mortality.
ICTSurF: Implicit Continuous-Time Survival Functions with Neural Networks
44ream/ictsurf
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Survival analysis is a widely known method for predicting the likelihood of an event over time.
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data
This paper proposes a novel approach to identify cancer subtypes through the integration of multi-omics data for clustering.
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information
According to the survival analysis results on 14 cancer types, Gene-MOE outperformed state-of-the-art models on 12 cancer types.
SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records
davidlee1102/surtimesurvival
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This study introduces "SurvTimeSurvival: Survival Analysis On Patients With Multiple Visits/Records", utilizing the Transformer model to not only handle the complexities of time-varying covariates but also covariates data.
Sensitivity of Survival Analysis Metrics
The specificity of the survival analysis data includes the distribution of events over time and the proportion of classes.
A Study on Survival Analysis Methods Using Neural Network to Prevent Cancers
ngocdung03/nDeep
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Cancers 2023
Early personalized prediction of cancer incidence is crucial for the population at risk.
A survey of Transformer applications for histopathological image analysis: New developments and future directions
Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs).
NSOTree: Neural Survival Oblique Tree
xs018/nsotree
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In this paper, we leverage the strengths of both neural networks and tree-based methods, capitalizing on their ability to approximate intricate functions while maintaining interpretability.
