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Abstract：
Multiple Sclerosis (MS) is a chronic disease developed in the human brain and spinal cord, which can cause permanent damage or deterioration of the nerves. The severity of MS disease is monitored by the Expanded Disability Status Scale, composed of several functional sub-scores. Early and accurate classification of MS disease severity is critical for slowing down or preventing disease progression via applying early therapeutic intervention strategies. Recent advances in deep learning and the wide use of Electronic Health Records (EHR) create opportunities to apply data-driven and predictive modeling tools for this goal. Previous studies focusing on using single-modal machine learning and deep learning algorithms were limited in terms of prediction accuracy due to data insufficiency or model simplicity. In this paper, we proposed the idea of using patients\' multimodal longitudinal and longitudinal EHR data to predict multiple sclerosis disease severity in the future. Our contribution has two main facets. First, we describe a pioneering effort to integrate structured EHR data, neuroimaging data and clinical notes to build a multi-modal deep learning framework to predict patient\'s MS severity. The proposed pipeline demonstrates up to 19% increase in terms of the area under the Area Under the Receiver Operating Characteristic curve (AUROC) compared to models using single-modal data. Second, the study also provides valuable insights regarding the amount useful signal embedded in each data modality with respect to MS disease prediction, which may improve data collection processes.
摘要：
多发性硬化症（MS）是一种在人脑和脊髓中发展的慢性疾病，这会导致神经的永久性损伤或恶化。MS疾病的严重程度由扩展残疾状态量表监测，由几个功能子分数组成。MS疾病严重程度的早期和准确分类对于通过应用早期治疗干预策略减缓或预防疾病进展至关重要。深度学习的最新进展和电子健康记录（EHR）的广泛使用为应用数据驱动和预测建模工具实现这一目标创造了机会。由于数据不足或模型简单，以前专注于使用单模态机器学习和深度学习算法的研究在预测准确性方面受到限制。在本文中,我们提出了使用患者多模态纵向和纵向EHR数据预测未来多发性硬化疾病严重程度的想法.我们的贡献有两个主要方面。首先,我们描述了整合结构化EHR数据的开创性努力，神经影像学数据和临床笔记，以构建多模式深度学习框架来预测患者的MS严重程度。与使用单模态数据的模型相比，拟议的管道在接收器工作特性曲线（AUROC）下的面积方面增加了19％。第二,该研究还提供了有关MS疾病预测的每种数据模态中嵌入的有用信号量的宝贵见解，这可能会改善数据收集过程。
