mlsanityData to 7 Oct 2026

Remote Medication Status Prediction for Individuals with Parkinson's Disease using Time-series Data from Smartphones

arXiv 2207.13700 ↗First posted 26 Jul 2022cs.LG cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 82 references that are in the map. First posted on arXiv 26 Jul 2022.

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References · 31

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