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Volume 2017, Article ID 7120691, 11 pages
Research Article

Sparse Learning of the Disease Severity Score for High-Dimensional Data

1Signals and Systems Department, School of Electrical Engineering, University of Belgrade, Bulevar Kralja Aleksandra 73, 11120 Belgrade, Serbia
2Center for Data Analytics and Biomedical Informatics, College of Science and Technology, Temple University, 1925 North 12th Street, Philadelphia, PA 19122, USA

Correspondence should be addressed to Zoran Obradovic; ude.elpmet@civodarbo.naroz

Received 11 May 2017; Revised 6 November 2017; Accepted 27 November 2017; Published 18 December 2017

Academic Editor: Sergio Gómez

Copyright © 2017 Ivan Stojkovic and Zoran Obradovic. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding. Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed. Given the rapid increase in both quantity and diversity of data measured and stored, the large amount of information is becoming one of the challenges for learning algorithms. In this work, we investigated the direction of the problem where the dimensionality of measured variables is large. Learning the severity score in such cases brings the issue of which of measured features are relevant. We have proposed a novel approach by combining desirable properties of existing formulations, which compares favorably to alternatives in accuracy and especially in the robustness of the learned scoring function. The proposed formulation has a nonsmooth penalty that induces sparsity. This problem is solved by addressing a dual formulation which is smooth and allows an efficient optimization. The proposed approach might be used as an effective and reliable tool for both scoring function learning and biomarker discovery, as demonstrated by identifying a stable set of genes related to influenza symptoms’ severity, which are enriched in immune-related processes.