Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
This addresses remote health monitoring for Parkinson's patients, offering a personalized and timely solution, though it appears incremental as it applies an existing Transformer model to a specific dataset.
The paper tackles predicting medication status for Parkinson's disease patients using smartphone time-series data, achieving high AUC scores (0.95-0.976) for three status categories.
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.