1 40 1 Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. URL Address <a href="http://doi.org/10.1155/2013/873595" target="_blank" rel="noreferrer noopener">http://doi.org/10.1155/2013/873595</a> Pages 873595–873595 Volume 2013 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A gradient boosting algorithm for survival analysis via direct optimization of concordance index. Publisher An entity responsible for making the resource available Computational and mathematical methods in medicine Date A point or period of time associated with an event in the lifecycle of the resource 2013 1905-07 Subject The topic of the resource *Survival Analysis; Algorithms; Artificial Intelligence; Breast Neoplasms/*epidemiology/*mortality; Clinical; Databases; Decision Support Systems; Factual; Female; Humans; Internet; Models; Prognosis; Proportional Hazards Models; Software; Theoretical Creator An entity primarily responsible for making the resource Chen Yifei; Jia Zhenyu; Mercola Dan; Xie Xiaohui Description An account of the resource Survival analysis focuses on modeling and predicting the time to an event of interest. Many statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index) and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online. Identifier An unambiguous reference to the resource within a given context <a href="http://doi.org/10.1155/2013/873595" target="_blank" rel="noreferrer noopener">10.1155/2013/873595</a> Rights Information about rights held in and over the resource Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s). *Survival Analysis 2013 Algorithms Artificial Intelligence Breast Neoplasms/*epidemiology/*mortality Chen Yifei Clinical Computational and mathematical methods in medicine Databases Decision Support Systems Factual Female Humans Internet Jia Zhenyu Mercola Dan Models Prognosis Proportional Hazards Models Software Theoretical Xie Xiaohui