# Survival Analysis

68 papers with code • 0 benchmarks • 3 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 $f/S$.

Image: Kvamme et al.

## Libraries

Use these libraries to find Survival Analysis models and implementations
6 papers
471

# DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network

2 Jun 2016

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.

4

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.

4

# Deep Cox Mixtures for Survival Regression

16 Jan 2021

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.

4

# SAFE: A Neural Survival Analysis Model for Fraud Early Detection

12 Sep 2018

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.

3

# Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks

2 Mar 2020

We describe a new approach to estimating relative risks in time-to-event prediction problems with censored data in a fully parametric manner.

3

# An Efficient Training Algorithm for Kernel Survival Support Vector Machines

21 Nov 2016

Survival analysis is a fundamental tool in medical research to identify predictors of adverse events and develop systems for clinical decision support.

2

# Deep Learning for Patient-Specific Kidney Graft Survival Analysis

29 May 2017

An accurate model of patient-specific kidney graft survival distributions can help to improve shared-decision making in the treatment and care of patients.

2

# Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling

10 Jul 2017

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.

2

# Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework

17 Jan 2018

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.

2

# A Scalable Discrete-Time Survival Model for Neural Networks

2 May 2018

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.

2