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

arXiv: https://arxiv.org/abs/2207.13700 · HTML: https://mlsanity.com/paper/2207.13700/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 82 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 30 of 31)

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- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
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- [A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series](https://mlsanity.com/paper/1707.03321/) (2017)
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- [Patch Transformer for Multi-tagging Whole Slide Histopathology Images](https://mlsanity.com/paper/1906.04151/) (2019)
- [Topic-Enhanced Memory Networks for Personalised Point-of-Interest Recommendation](https://mlsanity.com/paper/1905.13127/) (2019)
- [Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series](https://mlsanity.com/paper/1905.13570/) (2019)
- [PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data](https://mlsanity.com/paper/1810.01485/) (2018)
- [Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data](https://mlsanity.com/paper/2009.11999/) (2020)
- [Facial expressions can detect Parkinson's disease: preliminary evidence from videos collected online](https://mlsanity.com/paper/2012.05373/) (2020)

## 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.