ArticleAlzheimer's & dementia (Amsterdam, Netherlands)
Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's.
Article 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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.Alzheimer's research & therapy · 2026Article
- Identifying dementia neuropathology using low-burden clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Is the MMSE enough for MCI? A narrative review of the usefulness of the MMSE.Frontiers in psychology · 2025Review
- Creating Chemiluminescence Signature Arrays Coupled with Machine Learning for Alzheimer's Disease Serum Diagnosis.Research (Washington, D.C.) · 2025Article
- Alzheimer's disease detection using data fusion with a deep supervised encoder.Frontiers in dementia · 2024Article
- Optimizing Machine Learning Models for Accessible Early Cognitive Impairment Prediction: A Novel Cost-effective Model Selection Algorithm.IEEE access : practical innovations, open solutions · 2024Article
- Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
3 authors.
Funding
Abstract
introductionTo reduce demands on expert time and improve clinical efficiency, we developed a framework to evaluate whether inexpensive, accessible data could accurately classify Alzheimer's disease (AD) clinical diagnosis and predict the likelihood of progression.
methodsWe stratified relevant data into three tiers: obtainable at primary care (low-cost), mostly available at specialty visits (medium-cost), and research-only (high-cost). We trained several machine learning models, including a hierarchical model, an ensemble model, and a clustering model, to distinguish between diagnoses of cognitively unimpaired, mild cognitive impairment, and dementia due to AD.
resultsAll models showed viable classification, but the hierarchical and ensemble models outperformed the conventional model. Classifier "error" was predictive of progression rates, and cluster membership identified subgroups with high and low risk of progression within 1.5 to 3 years. DISCUSSION: Accessible, inexpensive clinical data can be used to guide AD diagnosis and are predictive of current and future disease states. HIGHLIGHTS: Classification performance using cost-effective features was accurate and robustHierarchical classification outperformed conventional multinomial classificationClassification labels indicated significant changes in conversion risk at follow-upA clustering-classification method identified subgroups at high risk of decline.
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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.