ArticleNPJ digital medicine2025
Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Potential protein blood-based biomarkers for cognitive dysfunction in Parkinson's disease: a systematic review and network meta-analysis.Frontiers in aging neuroscience · 2026Pooled it
- NEXIM: A Nash Equilibrium-Based Framework for Stable Explainable AI in Medical Applications.medRxiv : the preprint server for health sciences · 2026Article
- Brain connectivity fingerprinting as a predictive biomarker of art therapy outcomes in Parkinson's disease.Research square · 2026Article
- Selective amygdala subregional atrophy in Parkinson's disease: from subjective cognitive decline to mild cognitive impairment.Frontiers in aging neuroscience · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Cognitive impairment is a frequent complication of Parkinson's disease (PD), affecting up to half of newly diagnosed patients. To improve early detection and risk assessment, we developed machine learning models using clinical data from three independent PD cohorts, which are (LuxPARK, PPMI, ICEBERG). Models were trained to predict mild cognitive impairment (PD-MCI) and subjective cognitive decline (SCD) using Explainable Artificial Intelligence (XAI) for classification and time-to-event analysis. Multi-cohort models showed greater performance stability over single-cohort models, while retaining competitive average performance. Age at diagnosis and visuospatial ability were identified as key predictors. Significant sex differences observed highlight the importance of considering sex-specific factors in cognitive assessment. Men were more likely to report SCD. Our findings highlight the potential of multi-cohort machine learning for early identification and personalized management of cognitive decline in PD.
Identifiers
What OpenQuestion holds
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.