Clinical Scores Prediction and Medication Adjustment for Course of Parkinson's Disease

Abstract

Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder worldwide, characterized by progressive motor and non-motor symptoms. Unfortunately, there are no definitive PD modifying therapies, so accurate course prediction in advance and appropriate medical adjustment are essential to slow down degenerative process from onset. This work addresses a novel challenge in PD course prediction specifically at month 60 (m60) of both motor and non-motor indicator utilizing Magnetic Resonance Imaging (MRI) and demographic data of previous years. A medication adjustment network based on Reinforcement Learning (RL) is utilized as an agent to simulate medication from professionals in a prediction environment. The proposed approach achieve a more accurate prediction on motor and non-motor simultaneously, demonstrating significant promise for longer-term PD course prediction compared to existing works. Furthermore, adjustable medication branch shows consistent with our advance result and provide possible guidance on medication for healthcare practitioners.