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

Abstract

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.

Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High‐Order Local Projection Denoising Method

“…The dynamic characteristics contained in one-dimensional nonlinear time series can be revealed and extracted by studying the motion characteristics and distribution of attractor in high-dimensional phase space. The advantage of highdimensional signal processing has also been verified in the fields biomedical science and communication engineering [22,23]. The noise can be filtered out by adopting different projection ways of high-dimensional phase space.…”