Optimizing Patient-Specific Medication Regimen Policies Using Wearable Sensors in Parkinson’s Disease

Matt Baucum (), Anahita Khojandi (), Rama Vasudevan () and Ritesh Ramdhani ()
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Matt Baucum: Department of Business Analytics, Information Systems, and Supply Chain, Florida State University, Tallahassee, Florida 32306
Anahita Khojandi: Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, Tennessee 37996
Rama Vasudevan: Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830
Ritesh Ramdhani: Department of Neurology, Zucker School of Medicine at Hofstra/Northwell, Great Neck, New York 11021

Management Science, 2023, vol. 69, issue 10, 5964-5982

Abstract: Effective treatment of Parkinson’s disease (PD) is a continual challenge for healthcare providers, and providers can benefit from leveraging emerging technologies to supplement traditional clinic care. We develop a data-driven reinforcement learning (RL) framework to optimize PD medication regimens through wearable sensors. We leverage a data set of n = 26 PD patients who wore wrist-mounted movement trackers for two separate six-day periods. Using these data, we first build and validate a simulation model of how individual patients’ movement symptoms respond to medication administration. We then pair this simulation model with an on-policy RL algorithm that recommends optimal medication types, timing, and dosages during the day while incorporating human-in-the-loop considerations on medication administration. The results show that the RL-prescribed medication regimens outperform physicians’ medication regimens, despite physicians having access to the same data as the RL agent. To validate our results, we assess our wearable-based RL medication regimens using n = 399 PD patients from the Parkinson’s Progression Markers Initiative data set. We show that the wearable-based RL medication regimens would lead to significant symptom improvement for these patients, even more so than training RL policies directly from this data set. In doing so, we show that RL models from even small data sets of wearable data can offer novel, generalizable clinical insights and medication strategies, which may outperform those derived from larger data sets without wearable data.

Keywords: wearable sensors; remote monitoring; reinforcement learning; chronic disease management; PPMI (search for similar items in EconPapers)
Date: 2023
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