ArticleScientific reports2023
Machine learning-based prediction of longitudinal cognitive decline in early Parkinson's disease using multimodal features.
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 26 papers, 3 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- 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 for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- The identification of cognitive impairment in Parkinson's disease using biofluids, neuroimaging, and artificial intelligence.Frontiers in neuroscience · 2024Pooled it
- Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-Movement disorders clinical practice · 2026Article
- NEXIM: A Nash Equilibrium-Based Framework for Stable Explainable AI in Medical Applications.medRxiv : the preprint server for health sciences · 2026Article
- Predicting Cognition and Affective Changes in Newly Diagnosed Parkinson's Disease Through Longitudinal Data-Driven Clustering.Journal of geriatric psychiatry and neurology · 2026Article
- Motor-Derived Digital Biomarkers for Identifying Low-MoCA Status in People with Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Predictive value of multimodal functional magnetic resonance imaging for cognitive impairment in patients with non-dialysis chronic kidney disease.Quantitative imaging in medicine and surgery · 2026Article
- Characteristics and mechanisms of cognitive impairment in Parkinson disease.Nature reviews. Neurology · 2026Review
- 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
- Screen, Sample, Stratify: Biomarkers and Machine Learning Compress Dementia Pathways.Biomedicines · 2026Article
- Chronic Kidney disease and cognitive frailty in aging: molecular crosstalk and clinical implications.Frontiers in aging neuroscience · 2026Review
- Construction of a mild cognitive impairment prediction model for Parkinson's disease patients on the basis of multimodal data.NPJ Parkinson's disease · 2025Article
- Parkinson's Disease: Bridging Gaps, Building Biomarkers, and Reimagining Clinical Translation.Cells · 2025Review
- 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
- The Rapid Online Cognitive Assessment for the Detection of Neurocognitive Disorder: Open-Label Study.Journal of medical Internet research · 2025Article
- Interpretable machine learning for cognitive impairment prediction in Parkinson's disease: a multicenter validation study with SHAP analysis.Frontiers in aging neuroscience · 2025Article
- A hybrid approach for pattern recognition and interpretation in age-related false memory.Frontiers in psychology · 2025Article
- Identifying network state-based Parkinson's disease subtypes using clustering and support vector machine models.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
Abstract
Patients with Parkinson's Disease (PD) often suffer from cognitive decline. Accurate prediction of cognitive decline is essential for early treatment of at-risk patients. The aim of this study was to develop and evaluate a multimodal machine learning model for the prediction of continuous cognitive decline in patients with early PD. We included 213 PD patients from the Parkinson's Progression Markers Initiative (PPMI) database. Machine learning was used to predict change in Montreal Cognitive Assessment (MoCA) score using the difference between baseline and 4-years follow-up data as outcome. Input features were categorized into four sets: clinical test scores, cerebrospinal fluid (CSF) biomarkers, brain volumes, and genetic variants. All combinations of input feature sets were added to a basic model, which consisted of demographics and baseline cognition. An iterative scheme using RReliefF-based feature ranking and support vector regression in combination with tenfold cross validation was used to determine the optimal number of predictive features and to evaluate model performance for each combination of input feature sets. Our best performing model consisted of a combination of the basic model, clinical test scores and CSF-based biomarkers. This model had 12 features, which included baseline cognition, CSF phosphorylated tau, CSF total tau, CSF amyloid-beta
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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.