Investigating causal networks of dementia using causal discovery and natural language processing models

Le résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.