ReviewAlzheimer's research & therapy2024
Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.
Review in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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
27 citing papers in PubMed.
- Machine Learning Prediction of One-Year Rehospitalization in Dementia: A Retrospective Cohort Study.Health science reports · 2026Article
- A strategic pathway for the ethical development of AI tools in dementia care.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- A Mobile Application Intervention for Emotional Health in Older Adults: Designed for Dementia Prevention.Healthcare (Basel, Switzerland) · 2026Article
- EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.Alzheimer's research & therapy · 2026Article
- Explainable machine learning for predicting longitudinal dementia status: Establishing a leakage-free benchmark.PLOS digital health · 2026Article
- A digital twin methodology using retrospective patient data for sample size reduction in Alzheimer's disease clinical trials.Alzheimer's research & therapy · 2026Article
- Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease.Biomedicines · 2026Article
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Advancing global dementia research through equity and inclusion.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Enhanced diagnostic interpretation of the MoCA using machine learning.Frontiers in neuroscience · 2026Article
- Novel predictors of Alzheimer's disease in Down syndrome identified using machine learning.Journal of Alzheimer's disease : JAD · 2025Article
- A digital twin methodology using real patient data for sample size reduction in Alzheimer's disease randomized controlled clinical trials.medRxiv : the preprint server for health sciences · 2025Article
- Ocular changes as potential biomarkers for early diagnosis of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Development and validation of a model to predict the progression of Alzheimer's disease.Age and ageing · 2025Article
- Alzheimer's disease diagnosis among dementia patients via blood biomarker measurement based on the AT(N) system.Chinese medical journal · 2025Article
- Article
- Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer's disease progression study.BioData mining · 2025Article
- Cognitive performance classification of older patients using machine learning and electronic medical records.Scientific reports · 2025Article
- Development and Validation of a Tool to Predict Onset of Mild Cognitive Impairment and Alzheimer Dementia.JAMA network open · 2025Article
- Machine learning applied to mild cognitive impairment: bibliometric and visual analysis from 2015 to 2024.Frontiers in neurology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.
Indexed as
Identifiers
What Socratic 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 Socratic graph.