  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.
