论文标题
从智能手机数据诊断多发性硬化症的深度学习方法
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data
论文作者
论文摘要
多发性硬化症（MS）会出现各种症状，影响中枢神经系统。例如，MS可以引起疼痛，情绪和疲劳的变化，并可能损害一个人的运动，言语和视觉功能。 MS的诊断通常涉及复杂的临床评估和测试的组合，以排除其他症状相似的疾病。新技术，例如在自由生活条件下进行智能手机监测，可以通过量化长时间的症状存在和强度来客观地评估MS的症状。在这里，我们提出了一种深入学习方法，可以从智能手机衍生的数字生物标志物中诊断MS，该数字生物标志物使用多层感知器的新型组合以及神经软关注，以改善长期智能手机监控数据中模式的学习。使用来自774名参与者的队列的数据，我们证明了我们的深入学习模型能够区分有和没有MS的人，而在接收器操作特征曲线下有一个面积为0.88（95％CI：0.70，0.88）。我们的实验结果表明，从智能手机数据中得出的数字生物标志物将来可以用作MS的其他诊断标准。
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involves a combination of complex clinical assessments and tests to rule out other diseases with similar symptoms. New technologies, such as smartphone monitoring in free-living conditions, could potentially aid in objectively assessing the symptoms of MS by quantifying symptom presence and intensity over long periods of time. Here, we present a deep-learning approach to diagnosing MS from smartphone-derived digital biomarkers that uses a novel combination of a multilayer perceptron with neural soft attention to improve learning of patterns in long-term smartphone monitoring data. Using data from a cohort of 774 participants, we demonstrate that our deep-learning models are able to distinguish between people with and without MS with an area under the receiver operating characteristic curve of 0.88 (95% CI: 0.70, 0.88). Our experimental results indicate that digital biomarkers derived from smartphone data could in the future be used as additional diagnostic criteria for MS.
