ArticleScientific reports2021
Computational medication regimen for Parkinson's disease using reinforcement learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- A modified Delphi consensus study to support and enhance Parkinson's disease management.Journal of Parkinson's disease · 2026Article
- AI-Driven Analysis of Wrist-Worn Sensor Data for Monitoring Individual Treatment Response and Optimizing Levodopa Dosing in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Personalized medication recommendations for Parkinson's disease patients using gated recurrent units and SHAP interpretability.Scientific reports · 2025Article
- Addressing the need for standardization of symptomatic medication documentation in Parkinson's disease clinical research: A call to action.Journal of Parkinson's disease · 2025Review
- A Primer on Reinforcement Learning in Medicine for Clinicians.NPJ digital medicine · 2024Review
- Medication recommendation for Parkinson's disease based on dynamics of symptom progression.Scientific reports · 2024Article
- New horizons in prediction modelling using machine learning in older people's healthcare research.Age and ageing · 2024Article
- A decision support system based on recurrent neural networks to predict medication dosage for patients with Parkinson's disease.Scientific reports · 2024Article
- Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers.Journal of Parkinson's disease · 2024Article
- Article
- Using artificial intelligence to learn optimal regimen plan for Alzheimer's disease.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Using Artificial Intelligence to Learn Optimal Regimen Plan for Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
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Abstract
Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson's disease patients, the Parkinson's Progression Markers Initiative database, we derived clinically relevant disease states and an optimal combination of medications for each of them by using policy iteration of the Markov decision process (MDP). We focused on 8 combinations of medications, i.e., Levodopa, a dopamine agonist, and other PD medications, as possible actions and motor symptom severity, based on the Unified Parkinson Disease Rating Scale (UPDRS) section III, as reward/penalty of decision. We analyzed a total of 5077 visits from 431 PD patients with 55.5 months follow-up. We excluded patients without UPDRS III scores or medication records. We derived a medication regimen that is comparable to a clinician's decision. The RL model achieved a lower level of motor symptom severity scores than what clinicians did, whereas the clinicians' medication rules were more consistent than the RL model. The RL model followed the clinician's medication rules in most cases but also suggested some changes, which leads to the difference in lowering symptoms severity. This is the first study to investigate RL to improve the pharmacological approach of PD patients. Our results contribute to the development of an interactive machine-physician ecosystem that relies on evidence-based medicine and can potentially enhance PD management.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.