RT Journal Article
T1 Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science
A1 Montes-Torres, Julio
A1 Luis Subirats, Jose
A1 Ribelles, Nuria
A1 Urda, Daniel
A1 Franco, Leonardo
A1 Alba, Emilio
A1 Manuel Jerez, Jose
K1 Distributions
K1 Cancer
K1 Tests
AB One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.
PB Public library science
SN 1932-6203
YR 2016
FD 2016-08-17
LK http://hdl.handle.net/10668/19156
UL http://hdl.handle.net/10668/19156
LA en
DS RISalud
RD Apr 6, 2025