SynthesisJournal of medical systems2023
Machine Learning for Dementia Prediction: A Systematic Review and Future Research Directions.
Synthesis in Journal of medical systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 55 papers, 3 of them syntheses 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
55 citing papers in PubMed, 3 syntheses or guidelines pooled it, 166 citations in OpenAlex.
- Diagnostic utility of speech-based biomarkers in mild cognitive impairment: a systematic review and meta-analysis.Age and ageing · 2025Pooled it
- Clinical prediction models using artificial intelligence approaches in dementia.Aging clinical and experimental research · 2025Pooled it
- Machine Learning Driven by Magnetic Resonance Imaging for the Classification of Alzheimer Disease Progression: Systematic Review and Meta-Analysis.JMIR aging · 2024Pooled it
- From Fitness to Cognition: Machine-Learning Prediction of Cognitive Performance Using Physiological Parameters in Healthy Adults.Medicine and science in sports and exercise · 2026Article
- Machine Learning Prediction of One-Year Rehospitalization in Dementia: A Retrospective Cohort Study.Health science reports · 2026Article
- Article
- EEG-based biomarker for the identification of mild cognitive impairment via coherency analysis and iterative dynamic time warping.Scientific reports · 2026Article
- Predicting future dementia from routine clinical MRI and linked healthcare data.Alzheimer's research & therapy · 2026Article
- A dynamic risk prediction framework for Alzheimer's disease and related dementias with interpretability.NPJ digital medicine · 2026Article
- Diagnostic accuracy of digital clock drawing test for Alzheimer disease and mild cognitive impairment.NPJ digital medicine · 2026Article
- Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.npj aging · 2026Article
- Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.Alzheimer's research & therapy · 2026Article
- Predicting rates of cognitive and functional decline in Alzheimer's disease and mild cognitive impairment.Communications medicine · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- The role of brain health and resilience in reshaping trajectories of late-life neuropsychiatric disorders.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Liver Fibrosis and the Risks of Impaired Cognition and Dementia: Mechanisms, Evidence, and Clinical Implications.Medical sciences (Basel, Switzerland) · 2026Review
- Construction of a classification model for dementia among Brazilian adults aged 50 and over.Frontiers in aging neuroscience · 2026Article
- Clinical prediction rules for cognitive outcomes post-stroke: an updated systematic review and meta-analysis.EClinicalMedicine · 2025Article
- The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Detection of brain network abnormalities by graph invariants in Alzheimer's disease using MRI images.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nowadays, Artificial Intelligence (AI) and machine learning (ML) have successfully provided automated solutions to numerous real-world problems. Healthcare is one of the most important research areas for ML researchers, with the aim of developing automated disease prediction systems. One of the disease detection problems that AI and ML researchers have focused on is dementia detection using ML methods. Numerous automated diagnostic systems based on ML techniques for early prediction of dementia have been proposed in the literature. Few systematic literature reviews (SLR) have been conducted for dementia prediction based on ML techniques in the past. However, these SLR focused on a single type of data modality for the detection of dementia. Hence, the purpose of this study is to conduct a comprehensive evaluation of ML-based automated diagnostic systems considering different types of data modalities such as images, clinical-features, and voice data. We collected the research articles from 2011 to 2022 using the keywords dementia, machine learning, feature selection, data modalities, and automated diagnostic systems. The selected articles were critically analyzed and discussed. It was observed that image data driven ML models yields promising results in terms of dementia prediction compared to other data modalities, i.e., clinical feature-based data and voice data. Furthermore, this SLR highlighted the limitations of the previously proposed automated methods for dementia and presented future directions to overcome these limitations.
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.