2024-03-29T13:34:22Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72682023-03-17T13:01:26Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science
Montes-Torres, Julio
Subirats, José Luis
Ribelles, Nuria
Urda Muñoz, Daniel
Franco, Leonardo
Alba, Emilio
Jerez, José Manuel
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.
2023-01-18T12:05:05Z
2023-01-18T12:05:05Z
2016-08
info:eu-repo/semantics/article
http://hdl.handle.net/10259/7268
10.1371/journal.pone.0161135
1932-6203
eng
Plos One. 2016, V. 11, n. 8, e0161135
https://doi.org/10.1371/journal.pone.0161135
info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2008-2011/TIN2010-16556/ES/SISTEMAS INTELIGENTES BIOINSPIRADOS APLICADOS A MEDICINA PERSONALIZADA/
info:eu-repo/grantAgreement/Junta de Andalucía//P08-TIC-4026
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
Atribución 4.0 Internacional
Gang Han, Texas A&M University, United States