论文标题
通过使用多模式智能设备系统来发现帕金森氏病
Subgroup discovery of Parkinson's Disease by utilizing a multi-modal smart device system
论文作者
论文摘要
近年来，智能消费设备的传感器在运动障碍中显示出巨大的诊断潜力。在这种情况下，诸如电子问卷，手势和语音捕获之类的数据模式已成功捕获生物标志物，并允许歧视帕金森氏病（PD）（PD）和健康对照（HC）或差异诊断（DD）。但是，据我们所知，仍然缺乏对多模式智能设备系统评估的全面评估。在探索PD的一项前瞻性研究中，我们使用智能手表和智能手机从504名参与者（包括PD患者，DD和HC）收集多模式数据。这项研究旨在评估多模式与单模式数据对PD与HC和PD与DD分类的影响，以及用于亚组识别的PD组聚类。我们能够证明，通过结合各种方式，提高了分类精度并发现了进一步的PD簇。
In recent years, sensors from smart consumer devices have shown great diagnostic potential in movement disorders. In this context, data modalities such as electronic questionnaires, hand movement and voice captures have successfully captured biomarkers and allowed discrimination between Parkinson's disease (PD) and healthy controls (HC) or differential diagnosis (DD). However, to the best of our knowledge, a comprehensive evaluation of assessments with a multi-modal smart device system has still been lacking. In a prospective study exploring PD, we used smartwatches and smartphones to collect multi-modal data from 504 participants, including PD patients, DD and HC. This study aims to assess the effect of multi-modal vs. single-modal data on PD vs. HC and PD vs. DD classification, as well as on PD group clustering for subgroup identification. We were able to show that by combining various modalities, classification accuracy improved and further PD clusters were discovered.
