Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability

The enhanced Transformer model offers superior accuracy and efficiency in predicting cognitive decline compared to traditional models. Its capacity to process both continuous and categorical data and its interpretability through attention mechanisms make it a promising tool for early detection of neurodegenerative diseases, potentially improving clinical decision-making and interventions.

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Publication Details

Journal
BMC Public Health
Published
2025-02-07
DOI
https://doi.org/10.1186/s12889-025-21762-z
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43
Controls
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article

Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability

Mengyang Wang, Xinhang Huang, Junzhi Zhang, Kai Ma
1 citations
BMC Public Health
EEG and Brain-Computer Interfaces
1.43
article

Leveraging transformer models to predict cognitive impairment: accuracy, efficiency, and interpretability

Mengyang Wang, Xinhang Huang, Junzhi Zhang, Kai Ma
article en
1 citations

Abstract

The enhanced Transformer model offers superior accuracy and efficiency in predicting cognitive decline compared to traditional models. Its capacity to process both continuous and categorical data and its interpretability through attention mechanisms make it a promising tool for early detection of neurodegenerative diseases, potentially improving clinical decision-making and interventions.

BMC Public HealthVol. 25(1)
Tianjin University of Traditional Chinese Medicine (CN), China Agricultural University (CN)
Affordable and clean energy
Openalex Percentile: Top 23%
EEG and Brain-Computer Interfaces
1.43
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