Roy Subhrajit, Mincu Diana, Proleev Lev, Ghate Chintan, Graves Jennifer S, Steiner David F, Hartsell Fletcher Lee, Heller Katherine
Google Research, London, UK.
Department of Neurosciences, University of California, San Diego, San Diego, USA.
Sci Rep. 2025 May 25;15(1):18209. doi: 10.1038/s41598-024-63888-x.
Current care in multiple sclerosis (MS) primarily relies on infrequently obtained data such as magnetic resonance imaging, clinical laboratory tests or clinical history, resulting in subtle changes that may occur between visits being missed. Mobile technology enables continual collection of data and can pave the path for predicting complex aspects of MS such as symptoms and disease courses. To this end, we conducted a first-of-its-kind observational study called MS Mosaic. First, we developed and publicly launched a mobile app for collecting longitudinal data from MS subjects in the United States. Second, we ran the study across 3 years in order to capture complex patterns for this slow progressing disease. Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps/spasms) three months in advance.
目前，多发性硬化症（MS）的护理主要依赖于诸如磁共振成像、临床实验室检查或临床病史等不常获取的数据，这导致就诊期间可能出现的细微变化被遗漏。移动技术能够持续收集数据，并可为预测MS的复杂方面（如症状和病程）铺平道路。为此，我们开展了一项名为MS Mosaic的同类首创观察性研究。首先，我们开发并公开发布了一款移动应用程序，用于从美国的MS患者中收集纵向数据。其次，我们进行了为期3年的研究，以捕捉这种进展缓慢的疾病的复杂模式。最后，我们回顾性地开发了三种经典机器学习方法和两种深度学习模型，以提前三个月准确且持续地预测五种高严重程度症状（疲劳、感觉障碍、行走不稳、抑郁或焦虑以及抽筋/痉挛）的发生率。
