95 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.
These leaderboards are used to track progress in Survival Analysis
LibrariesUse these libraries to find Survival Analysis models and implementations
Most implemented papers
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network
We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations.
Adversarial Time-to-Event Modeling
Modern health data science applications leverage abundant molecular and electronic health data, providing opportunities for machine learning to build statistical models to support clinical practice.
Deep Cox Mixtures for Survival Regression
Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up.
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform.
Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks
We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner.
An Efficient Training Algorithm for Kernel Survival Support Vector Machines
Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support.
Deep Learning for Patient-Specific Kidney Graft Survival Analysis
An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.
Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling
Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis.
Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework
Survival analysis/time-to-event models are extremely useful as they can help companies predict when a customer will buy a product, churn or default on a loan, and therefore help them improve their ROI.
A Scalable Discrete-Time Survival Model for Neural Networks
It is important for predictive models to be able to use survival data, where each patient has a known follow-up time and event/censoring indicator.