ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2022
Automated text-level semantic markers of Alzheimer's disease.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Item-Level Analysis of Category Fluency Test Performance: A Systematic Review and Meta-Analysis of Studies of Normal and Neurologically Abnormal Ageing.Neuropsychology review · 2025Pooled it
- Article
- Semantic memory navigation in mild cognitive impairment: Automated markers with neural and biofluid correlates.NeuroImage · 2026Article
- Article
- Benchmarking speech biomarkers of Alzheimer's against cognitive and neural measures.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Digital language measures capture episodic memory disruptions in people with human immunodeficiency virus: A machine learning study.The Clinical neuropsychologist · 2026Article
- Speech and language outcome measures in clinical trials of Alzheimer's and Parkinson's diseases.Expert review of neurotherapeutics · 2026Review
- Cognitive Phenotyping of Parkinson's Disease Patients Via Digital Analysis of Spoken Word Properties.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.Journal of medical Internet research · 2025Article
- The structure of spontaneous speech changes in Alzheimer's disease: Crosslingual evidence from English and Greek.PloS one · 2025Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Advancements in dementia research, diagnostics, and care in Latin America: Highlights from the 2023 Alzheimer's Association International conference satellite symposium in Mexico City.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Neurocognitive correlates of semantic memory navigation in Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Automated free speech analysis reveals distinct markers of Alzheimer's and frontotemporal dementia.PloS one · 2024Article
- Speech and language markers of neurodegeneration: a call for global equity.Brain : a journal of neurology · 2023Review
- Models and Approaches for Comprehension of Dysarthric Speech Using Natural Language Processing: Systematic Review.JMIR rehabilitation and assistive technologies · 2023Review
- Review
- Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review.Frontiers in aging neuroscience · 2023Review
- Detecting Parkinson's disease and its cognitive phenotypes via automated semantic analyses of action stories.NPJ Parkinson's disease · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
introductionAutomated speech analysis has emerged as a scalable, cost-effective tool to identify persons with Alzheimer's disease dementia (ADD). Yet, most research is undermined by low interpretability and specificity.
methodsCombining statistical and machine learning analyses of natural speech data, we aimed to discriminate ADD patients from healthy controls (HCs) based on automated measures of domains typically affected in ADD: semantic granularity (coarseness of concepts) and ongoing semantic variability (conceptual closeness of successive words). To test for specificity, we replicated the analyses on Parkinson's disease (PD) patients.
resultsRelative to controls, ADD (but not PD) patients exhibited significant differences in both measures. Also, these features robustly discriminated between ADD patients and HC, while yielding near-chance classification between PD patients and HCs. DISCUSSION: Automated discourse-level semantic analyses can reveal objective, interpretable, and specific markers of ADD, bridging well-established neuropsychological targets with digital assessment tools.
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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.