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 with no code
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis
This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis.
Dynamical Survival Analysis with Controlled Latent States
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series.
Explainable AI for survival analysis: a median-SHAP approach
With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications.
High-Dimensional False Discovery Rate Control for Dependent Variables
In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples.
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
The objective of releasing this dataset is to give a broad range of researchers the possibility of working with real-world data from an internationally well-known company and introduce a standard benchmark to the predictive maintenance field, fostering reproducible research.
Survival Analysis of Young Triple-Negative Breast Cancer Patients
Triple-negative breast cancer (TNBC), lacking these receptors, accounts for about 15 percent of cases and is more prevalent in younger patients, often resulting in poorer outcomes.
TripleSurv: Triplet Time-adaptive Coordinate Loss for Survival Analysis
However, ranking loss only focus on the ranking of survival time and does not consider potential effect of samples for exact survival time values.
Composite Survival Analysis: Learning with Auxiliary Aggregated Baselines and Survival Scores
Survival Analysis (SA) constitutes the default method for time-to-event modeling due to its ability to estimate event probabilities of sparsely occurring events over time.
HEALNet -- Hybrid Multi-Modal Fusion for Heterogeneous Biomedical Data
Technological advances in medical data collection such as high-resolution histopathology and high-throughput genomic sequencing have contributed to the rising requirement for multi-modal biomedical modelling, specifically for image, tabular, and graph data.
Clinical Characteristics and Laboratory Biomarkers in ICU-admitted Septic Patients with and without Bacteremia
Both CRP and PCT showed a substantial area under the curve (AUC) value for discriminating bacteremia among septic patients (0. 757 and 0. 845, respectively).
