Document detail
ID
oai:arXiv.org:2207.13700
Topic
Computer Science - Machine Learnin... Computer Science - Artificial Inte...Author
Li, Weijian Zhu, Wei Dorsey, E. Ray Luo, JieboCategory
Computer Science
Year
2022
listing date
6/7/2023
Keywords
parkinson method disease remote statusAbstract
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.
;Comment: Accepted to ICDH-2023.
Camera ready with supplementary material
Li, Weijian,Zhu, Wei,Dorsey, E. Ray,Luo, Jiebo, 2022, Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones
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