论文标题
使用智能手机的时间序列数据对患有帕金森氏病的人的远程药物状态预测
Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
论文作者
论文摘要
帕金森氏病等神经疾病的药物通常会远离医院。这种外观环境在收集及时，准确的健康状况数据方面构成了挑战。从可穿戴传感器收集的行为信号中的个体差异也导致难以采用当前的通用机器学习分析管道。为了应对这些挑战，我们提出了一种使用公共MPower数据集预测帕金森氏病患者的药物状况的方法，该数据集包含62,182个远程多模式测试记录，该记录在487名患者的智能手机上收集。提出的方法在客观地预测三种药物状态时显示出令人鼓舞的结果：在药物（AUC = 0.95），药物（AUC = 0.958）之后（AUC = 0.958）以及另一个时间（AUC = 0.976）通过检查患者的历史记录，并使用通过变压器模型学到的注意力重量来研究患者的历史记录。我们的方法为及时有客观的方式提供了个性化远程健康感知的创新方式，可以使广泛的类似应用受益。
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.
