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
TorchSurv: A Lightweight Package for Deep Survival Analysis
TorchSurv is a Python package that serves as a companion tool to perform deep survival modeling within the PyTorch environment.
Probabilistic Survival Analysis by Approximate Bayesian Inference of Neural Networks
thecml/baysurv
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In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a focus on prediction and calibration performance.
iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer
The limited availability of modalities for each patient would cause information loss, adversely affecting predictive accuracy.
Interpretable Machine Learning for Survival Analysis
With the spread and rapid advancement of black box machine learning models, the field of interpretable machine learning (IML) or explainable artificial intelligence (XAI) has become increasingly important over the last decade.
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patient care.
Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data
We applied our approach to sites with heterogeneous survival data originating from emergency departments in Singapore and the United States.
Optimal Sparse Survival Trees
Interpretability is crucial for doctors, hospitals, pharmaceutical companies and biotechnology corporations to analyze and make decisions for high stakes problems that involve human health.
Optimal Survival Trees: A Dynamic Programming Approach
Survival analysis studies and predicts the time of death, or other singular unrepeated events, based on historical data, while the true time of death for some instances is unknown.
Robust Survival Analysis with Adversarial Regularization
mlpotter/sawar
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Survival Analysis (SA) is about modeling the time for an event of interest to occur, which has important applications in many fields, including medicine, defense, finance, and aerospace.
Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees
Censoring is the central problem in survival analysis where either the time-to-event (for instance, death), or the time-tocensoring (such as loss of follow-up) is observed for each sample.
