ReviewAlzheimer's & dementia (Amsterdam, Netherlands)
Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.
Review in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Electronic health record-based prediction models for dementia detection: a systematic review of model performance and quality.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Quality of Life of People Living With Dementia Residing in Nursing Homes: Secondary Analysis of Observational Data.Journal of medical Internet research · 2026Article
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- Cross-Lingual Alzheimer's Disease Speech Detection: Polarity Inversion and Few-Shot Calibration Strategies.Bioengineering (Basel, Switzerland) · 2026Article
- Early Detection of Alzheimer's Disease and Related Dementias From Spontaneous Speech Using Foundation Speech and Language Models: Comparative Evaluation.JMIR formative research · 2026Article
- Computational Analysis of Expressive Behavior in Clinical Assessment.Annual review of clinical psychology · 2026Review
- Cross-Modal Alignment and Rectified Flow-Based Latent Representation Synthesis for Enhanced Speech-Driven Alzheimer's Disease Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Simulated depression risk classification from Parkinson's voice features using a self-attention-enhanced MLP architecture.Scientific reports · 2026Article
- Speech and language outcome measures in clinical trials of Alzheimer's and Parkinson's diseases.Expert review of neurotherapeutics · 2026Review
- Automated Speech Markers of Alzheimer Dementia: Test of Cross-Linguistic Generalizability.Journal of medical Internet research · 2025Article
- Voiceprints of cognitive impairment: analyzing digital voice for early detection of Alzheimer's and related dementias.NPJ dementia · 2025Article
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
- Statistical analysis of interpretable linguistic features for MCI detection in bilingual speech.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAlzheimer's disease (AD) prevalence is increasing, with no current cure. Natural language processing (NLP) offers the potential for non-invasive diagnostics, social burden assessment, and research advancements in AD.
methodA systematic review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines explored NLP applications in AD, focusing on dataset types, sources, research foci, methods, and effectiveness. Searches were conducted across six databases (ACM, Embase, IEEE, PubMed, Scopus, and Web of Science) from January 2020 to July 2024.
resultsOf 1740 records, 79 studies were selected. Frequently used datasets included speech and electronic health records (EHR), along with social media and scientific publications. Machine learning and neural networks were primarily applied to speech, EHR, and social media data, while rule-based methods were used to analyze literature datasets. DISCUSSION: NLP has proven effective in various aspects of AD research, including diagnosis, monitoring, social burden assessment, biomarker analysis, and research. However, there are opportunities for improvement in dataset diversity, model interpretability, multilingual capabilities, and addressing ethical concerns. Highlights: This review systematically analyzed 79 studies from six major databases, focusing on the advancements and applications of natural language processing (NLP) in Alzheimer's disease (AD) research.The study highlights the need for models focusing on remote monitoring of AD patients using speech analysis, offering a cost-effective alternative to traditional methods such as brain imaging and aiding clinicians in both prediagnosis and post-diagnosis periods.The use of pretrained multilingual models is recommended to improve AD detection across different languages by leveraging diverse speech features and utilizing publicly available datasets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.