Medication for neurological diseases such as the Parkinson's disease usually happens remotely away from hospitals. Such out-of-lab environments pose challenges in collecting timely and accurate health status data. Individual differences in behavioral signals collected from wearable sensors also lead to difficulties in adopting current general machine learning analysis pipelines. To address these challenges, we present a method for predicting the medication status of Parkinson's disease patients using the public mPower dataset, which contains 62,182 remote multi-modal test records collected on smartphones from 487 patients. The proposed method shows promising results in predicting three medication statuses objectively: Before Medication (AUC=0.95), After Medication (AUC=0.958), and Another Time (AUC=0.976) by examining patient-wise historical records with the attention weights learned through a Transformer model. Our method provides an innovative way for personalized remote health sensing in a timely and objective fashion which could benefit a broad range of similar applications.

翻译：对于帕金森病等神经系统疾病，患者通常需在医院外的远程环境中用药。这种非实验室环境给及时、准确地采集健康状态数据带来了挑战。从可穿戴传感器采集的行为信号存在个体差异，导致难以采用通用的机器学习分析流程。针对这些问题，我们提出了一种利用公开mPower数据集预测帕金森病患者用药状态的方法。该数据集包含487名患者通过智能手机采集的62,182条远程多模态测试记录。通过Transformer模型学习注意力权重，分析患者个体历史记录，本方法在客观预测三种用药状态时展现出优异性能：用药前（AUC=0.95）、用药后（AUC=0.958）及其他时段（AUC=0.976）的预测准确率。该方法为个性化远程健康感知提供了一种及时、客观的创新方案，可广泛适用于类似应用场景。