2026-06-30T02:18:07Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72682023-03-17T13:01:26Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Montes-Torres, Julio Subirats, José Luis Ribelles, Nuria Urda Muñoz, Daniel Franco, Leonardo Alba, Emilio Jerez, José Manuel 2023-01-18T12:05:05Z 2023-01-18T12:05:05Z 2016-08 http://hdl.handle.net/10259/7268 10.1371/journal.pone.0161135 1932-6203 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. 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. application/pdf eng Gang Han, Texas A&M University, United States 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 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Informática Computer science Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion PLOS ONE 11 8