ArticleScientific reports2023
Prediction of dyskinesia in Parkinson's disease patients using machine learning algorithms.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- A multimodal explainable artificial intelligence framework for interpretable Parkinson's disease prediction.Scientific reports · 2026Article
- Relationship between frailty and disease progression in Parkinson's disease: a 3-year longitudinal study.GeroScience · 2026Article
- Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson's Disease at Risk of Gait Deterioration.Bioengineering (Basel, Switzerland) · 2026Article
- A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson's Disease and Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Baseline [NPJ Parkinson's disease · 2025Article
- Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach.Brain and behavior · 2025Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Dyskinesias are non preventable abnormal involuntary movements that represent the main challenge of the long term treatment of Parkinson's disease (PD) with the gold standard dopamine precursor levodopa. Applying machine learning techniques on the data extracted from the Parkinson's Progression Marker Initiative (PPMI, Michael J. Fox Foundation), this study was aimed to identify PD patients who are at high risk of developing dyskinesias. Data regarding clinical, behavioral and neurological features from 697 PD patients were included in our study. Our results show that the Random Forest was the classifier with the best and most consistent performance, reaching an area under the receiver operating characteristic (ROC) curve of up to 91.8% with only seven features. Information regarding the severity of the symptoms, the semantic verbal fluency, and the levodopa treatment were the most important for the prediction, and were further used to create a Decision Tree, whose rules may guide the pharmacological management of PD symptoms. Our results contribute to the identification of PD patients who are prone to develop dyskinesia, and may be considered in future clinical trials aiming at developing new therapeutic approaches for PD.
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Registered trials
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.