ArticleFrontiers in aging neuroscience2023
Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation.
Article in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled 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
- 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
- Physiological classification of Parkinson's disease severity using multimodal speech biomarkers with a hybrid CNN-Mamba framework.Frontiers in physiology · 2026Article
- Interpretable and explainable predictive machine learning models for gastric retention before gastroscopy.Frontiers in medicine · 2026Article
- A Machine Learning Approach to Modify the Neurocognitive Frailty Index for the Prediction of Cognitive Status in the Canadian Population.Journal of clinical medicine · 2025Article
- An Explainable Approach to Parkinson's Diagnosis Using the Contrastive Explanation Method-CEM.Diagnostics (Basel, Switzerland) · 2025Article
- Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer's disease spectrum: demonstration analyses in the COMPASS-ND study.Frontiers in aging neuroscience · 2025Article
- Risk factors and predictors for Lewy body dementia: a systematic review.NPJ dementia · 2025Article
- Multimodal neuroimaging-based prediction of Parkinson's disease with mild cognitive impairment using machine learning technique.NPJ Parkinson's disease · 2024Article
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
- Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study.BMC geriatrics · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not. Method: Participants were 48 well-characterized PD patients ( Results: An excellent RF model discriminated baseline PDID from PDND ( Conclusion: Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.
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