ArticleFrontiers in public health2021
Improving Alzheimer's Disease Detection for Speech Based on Feature Purification Network.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review.IEEE journal of biomedical and health informatics · 2026Pooled it
- Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study.JMIR medical informatics · 2026Article
- Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.Scientific reports · 2025Article
- A Systematic Review of Natural Language Processing Techniques for Early Detection of Cognitive Impairment.Mayo Clinic proceedings. Digital health · 2025Review
- Exploring the Kidney-Brain Crosstalk: Biomarkers for Early Detection of Kidney Injury-Related Alzheimer's Disease.Journal of inflammation research · 2025Article
- The Emerging Science of Interacting Minds.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Article
- Learning implicit sentiments in Alzheimer's disease recognition with contextual attention features.Frontiers in aging neuroscience · 2023Article
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease involving the decline of cognitive ability with illness progresses. At present, the diagnosis of AD mainly depends on the interviews between patients and doctors, which is slow, expensive, and subjective, so it is not a better solution to recognize AD using the currently available neuropsychological examinations and clinical diagnostic criteria. A recent study has indicated the potential of language analysis for AD diagnosis. In this study, we proposed a novel feature purification network that can improve the representation learning of transformer model further. Though transformer has made great progress in generating discriminative features because of its long-distance reasoning ability, there is still room for improvement. There exist many common features that are not indicative of any specific class, and we rule out the influence of common features from traditional features extracted by transformer encoder and can get more discriminative features for classification. We apply this method to improve transformer's performance on three public dementia datasets and get improved classification results markedly. Specifically, the method on Pitt datasets gets state-of-the-art (SOTA) result.
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