Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

S. Pölsterl, N. Navab, A. Katouzian
Fast Training of Support Vector Machines for Survival Analysis
Machine Learning and Knowledge Discovery in Databases (bib)

Springer Link:

Source Code:

Survival analysis is a commonly used technique to identify important predictors of adverse events and develop guidelines for patient's treatment in medical research. When applied to large amounts of patient data, efficient optimization routines become a necessity. In this paper, we propose efficient training algorithms for three kinds of linear survival support vector machines: 1) ranking-based, 2) regression-based, and 3) combined ranking and regression. We perform optimization in the primal using truncated Newton optimization and use order statistic trees to lower computational costs of training survival models. We employ the same optimization technique and extend it for non-linear models too. Our results demonstrate the superiority of our proposed optimization scheme over existing training algorithms, which fail due to their inherently high time and space complexities when applied to large medical datasets. We validate the proposed survival models on 6 real-world datasets, and show that pure ranking-based approaches outperform regression and hybrid models.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif