Computational Statistics and Machine Learning (CSML), Istituto Italiano di Tecnologia, Genova, Italy.
Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, UK; Department of Computer Science, University College London, United Kingdom.
Neuroimage. 2019 Jul 15;195:215-231. doi: 10.1016/j.neuroimage.2019.01.053. Epub 2019 Mar 17.
Combining neuroimaging and clinical information for diagnosis, as for example behavioral tasks and genetics characteristics, is potentially beneficial but presents challenges in terms of finding the best data representation for the different sources of information. Their simple combination usually does not provide an improvement if compared with using the best source alone. In this paper, we proposed a framework based on a recent multiple kernel learning algorithm called EasyMKL and we investigated the benefits of this approach for diagnosing two different mental health diseases. The well known Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset tackling the Alzheimer Disease (AD) patients versus healthy controls classification task, and a second dataset tackling the task of classifying an heterogeneous group of depressed patients versus healthy controls. We used EasyMKL to combine a huge amount of basic kernels alongside a feature selection methodology, pursuing an optimal and sparse solution to facilitate interpretability. Our results show that the proposed approach, called EasyMKLFS, outperforms baselines (e.g. SVM and SimpleMKL), state-of-the-art random forests (RF) and feature selection (FS) methods.
将神经影像学和临床信息（如行为任务和遗传特征）结合起来进行诊断，可能会带来益处，但在寻找不同信息源的最佳数据表示形式方面存在挑战。简单地将它们组合通常不会比单独使用最佳信息源有所改进。在本文中，我们提出了一个基于最近的多内核学习算法（称为 EasyMKL）的框架，并研究了这种方法在诊断两种不同的心理健康疾病方面的益处。该框架使用了著名的阿尔茨海默病神经影像学倡议（ADNI）数据集，用于处理阿尔茨海默病（AD）患者与健康对照的分类任务，以及第二个数据集，用于处理将异质抑郁患者与健康对照进行分类的任务。我们使用 EasyMKL 结合了大量的基本内核以及特征选择方法，旨在找到一个最优和稀疏的解决方案，以促进可解释性。我们的结果表明，称为 EasyMKLFS 的方法优于基线（例如 SVM 和 SimpleMKL）、最先进的随机森林（RF）和特征选择（FS）方法。
