SurvSet: An open-source time-to-event dataset repository

TL;DR

SurvSet is an open-source repository providing 76 standardized time-to-event datasets to facilitate benchmarking and development of machine learning and statistical methods in survival analysis.

Contribution

It introduces the first comprehensive, consistently formatted open-source T2E dataset repository supporting rapid benchmarking of ML algorithms.

Findings

76 datasets included, covering diverse domains

Standardized formatting enables uniform preprocessing

Facilitates benchmarking and development of T2E methods

Abstract

Time-to-event (T2E) analysis is a branch of statistics that models the duration of time it takes for an event to occur. Such events can include outcomes like death, unemployment, or product failure. Most modern machine learning (ML) algorithms, like decision trees and kernel methods, are supported for T2E modelling with data science software (python and R). To complement these developments, SurvSet is the first open-source T2E dataset repository designed for a rapid benchmarking of ML algorithms and statistical methods. The data in SurvSet have been consistently formatted so that a single preprocessing method will work for all datasets. SurvSet currently has 76 datasets which vary in dimensionality, time dependency, and background (the majority of which come from biomedicine). SurvSet is available on PyPI and can be installed with pip install SurvSet. R users can download the data directly from the corresponding git repository.