ArticleNPJ dementia2025
Investigating causal networks of dementia using causal discovery and natural language processing models.
Article in NPJ dementia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
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Who cites it
3 citing papers in PubMed.
- Causal Discovery in Observational Medical Research: Scoping Review.JMIR medical informatics · 2026Article
- Uncovering the Role of Neuropsychiatric Symptoms in Cognitive Impairment Progression.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- Comparison of physical characteristics among english professional and semi-professional soccer players across different leagues.PloS one · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Comprehensively studying modifiable risk factors to understand their contributions to dementia mechanisms is imperative. This study used natural language processing (NLP) models to pre-select candidate risk factors for dementia from 5505 baseline variables in the UK Biobank. We then applied causal discovery approaches to examine the relationships among the selected variables and their links to dementia in later life, presenting these connections in a causal network. We identified eight risk factors that directly or indirectly influence dementia, with mental disorders due to brain dysfunction (ICD-10 F06) acting as direct causes and mediators in pathways from other neurological disorders to dementia. Although evidence for the direct link between biological age and dementia was less pronounced, its potential value in dementia management remains non-negligible. This study advances our understanding of dementia mechanisms and highlights the potential of NLP and machine learning for the causal discovery of complex diseases from high-dimensional data.
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Registered trials
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