ArticleNPJ Parkinson's disease2022
Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease.
Article in NPJ Parkinson's disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 5 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Interrogating DNA methylation associated with Lewy body pathology in a cross brain-region and multi-cohort study.Nature communications · 2026Pooled it
- The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.Frontiers in neuroscience · 2024Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-Movement disorders clinical practice · 2026Article
- Motor-Derived Digital Biomarkers for Identifying Low-MoCA Status in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Monoaminergic Networks of Cognitive and Behavioral Symptoms in Early Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Assessment of mental and behavioural non-motor symptoms of Parkinson's Disease using Artificial Intelligence (AI): a systematic review.Communications medicine · 2026Article
- Characteristics and mechanisms of cognitive impairment in Parkinson disease.Nature reviews. Neurology · 2026Review
- Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.NPJ digital medicine · 2025Article
- Predicting Cognitive Decline in Parkinson's Disease Using Artificial Neural Networks: An Explainable AI Approach.Brain sciences · 2025Article
- Predicting dementia in people with Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.European journal of neurology · 2025Observational
- Epigenetic insights into neuropsychiatric and cognitive symptoms in Parkinson's disease: A DNA co-methylation network analysis.NPJ Parkinson's disease · 2025Article
- Multimodal neuroimaging-based prediction of Parkinson's disease with mild cognitive impairment using machine learning technique.NPJ Parkinson's disease · 2024Article
- Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.Clinical and translational science · 2024Article
- Blood-based multivariate methylation risk score for cognitive impairment and dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Deep Learning for Parkinson's Disease Diagnosis: A Graph Neural Network (GNN) Based Classification Approach with Graph Wavelet Transform (GWT) Using Protein-Peptide Datasets.Diagnostics (Basel, Switzerland) · 2024Article
- Classification performance assessment for imbalanced multiclass data.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Cognitive impairment is a debilitating symptom in Parkinson's disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson's Progression Markers Initiative (PPMI). Annual cognitive assessments over an 8-year time span were used to define two cognitive outcomes of (i) cognitive impairment, and (ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables.
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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.