RT info:eu-repo/semantics/article
T1 Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science
A1 Montes-Torres, Julio
A1 Subirats, José Luis
A1 Ribelles, Nuria
A1 Urda Muñoz, Daniel
A1 Franco, Leonardo
A1 Alba, Emilio
A1 Jerez, José Manuel
K1 Informática
K1 Computer science
AB One of the prevailing applications of machine learning is the use of predictive modelling inclinical 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 thefield of machine learning to biomedical researchers. We propose a web based software forsurvival analysis called OSA (Online Survival Analysis), which has been developed as anopen access and user friendly option to obtain discrete time, predictive survival models atindividual 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 hazardcurves with multiple options to personalise the plots, obtain contingency tables from theuploaded data to perform different tests, and fit a Cox regression model from a number ofpredictor variables. In the Materials and Methods section, we depict the general architectureof the application and introduce the mathematical background of each of the implementedmethods. The study concludes with examples of use showing the results obtained with public datasets.
PB Gang Han, Texas A&M University, United States
YR 2016
FD 2016-08
LK http://hdl.handle.net/10259/7268
UL http://hdl.handle.net/10259/7268
LA eng
NO This work was supported by grants TIN2010-16556 from MICINN-SPAIN (Spanish Government) and P08-TIC-4026 (Andalusia Regional Government, Spain). All of them include FEDER funds (European Union). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024