使用智能手机对多发性硬化进行个性化纵向评估
Personalized Longitudinal Assessment of Multiple Sclerosis Using Smartphones
摘要：
个性化的纵向疾病评估是快速诊断、适当管理和最佳适应多发性硬化症（MS）治疗策略的核心。这对于鉴定特异质受试者特异性疾病特征也很重要。在这里，我们设计了一种新的纵向模型，使用可能包含缺失值的传感器数据以自动方式绘制个体疾病轨迹。首先，我们收集与步态和平衡相关的数字测量，以及使用智能手机上管理的基于传感器的评估的上肢功能。接下来，我们通过插补处理缺失数据。然后，我们发现潜在的标记MS采用广义估计方程。随后，从多个训练数据集学习的参数被集合起来，形成一个简单的、统一的纵向预测模型，以预测MS随着时间的推移在以前看不见的MS患者中的MS。结果表明，所提出的模型有望实现个性化的纵向MS评估;他们还表明，从基于传感器的评估中远程收集的与步态和平衡以及上肢功能相关的特征可能是预测MS的有用数字标记。
英文摘要：
Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying the idiosyncratic subject-specific disease profiles. Here, we design a novel longitudinal model to map individual disease trajectories in an automated way using sensor data that may contain missing values. First, we collect digital measurements related to gait and balance, and upper extremity functions using sensor-based assessments administered on a smartphone. Next, we treat missing data via imputation. We then discover potential markers of MS by employing a generalized estimation equation. Subsequently, parameters learned from multiple training datasets are ensembled to form a simple, unified longitudinal predictive model to forecast MS over time in previously unseen people with MS. To mitigate potential underestimation for individuals with severe disease scores, the final model incorporates additional subject-specific fine-tuning using data from the first day. The results show that the proposed model is promising to achieve personalized longitudinal MS assessment; they also suggest that features related to gait and balance as well as upper extremity function, remotely collected from sensor-based assessments, may be useful digital markers for predicting MS over time.
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