ArticleBMJ open2023
Differentiating Alzheimer's disease from mild cognitive impairment: a quick screening tool based on machine learning.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- Metabolic status and brain health: the relationship between the metabolic score for insulin resistance and cognitive function in older adults.European journal of medical research · 2025Article
- Latent Profiles Based on Combined Risk Factors for Cognitive Decline in European Older Adults: A Retrospective Study Based on the SHARE HCAP Project.Neurology international · 2025Article
- Development and validation of a combined clinical and MRI-based biomarker model to differentiate mild cognitive impairment from mild Alzheimer's disease.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Comparison of Deep Learning and Traditional Machine Learning Models for Predicting Mild Cognitive Impairment Using Plasma Proteomic Biomarkers.International journal of molecular sciences · 2025Article
- Dimensions of the psychological emptiness and its relation with suicide history among college students.Frontiers in psychology · 2025Article
- Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.Brain sciences · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary.
methodsA total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool.
resultsThe Alzheimer's Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models.
conclusionThe quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.
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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.