Development and Validation of a Machine Learning-Based Dementia Screening Tool: The Six-Question Dementia Screening Test.

Meng-Tien Wu, Kuan-Ying Li, Ching-Fang Chien, Ling-Chun Huang, Chen-Wen Yen, Yuan-Han Yang

Abstract readValidation Study

In one paragraph

Article in American journal of Alzheimer's disease and other dementias. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

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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.

2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Meng-Tien WuSchool of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.ORCID 0000-0002-5054-7659

Kuan-Ying LiDepartment of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.ORCID 0000-0003-3785-4109

Ching-Fang ChienDepartment of Neurology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan.

Ling-Chun HuangSchool of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.

Chen-Wen YenDepartment of Mechanical and Electro-Mechanical Engineering, National Sun Yat-Sen University, Kaohsiung, Taiwan.

Yuan-Han YangSchool of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.ORCID 0000-0002-1918-9021

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Timely detection of dementia is crucial for reducing its health and societal burden. Standard tools such as the Mini-Mental State Examination (MMSE) and Cognitive Abilities Screening Instrument (CASI), although widely used, are limited by time and resource demands. This study developed and validated a machine learning-based screening tool using the Six-Question Dementia Screening Test (6Q-DS), a brief interview of six items. Data from 533 older adults at a neurology clinic in Taiwan (331 with dementia, 202 without) were analyzed with eXtreme Gradient Boosting. The 6Q-DS achieved an AUC of 0.936, sensitivity 0.879, specificity 0.951, and accuracy 0.907 for dementia vs non-dementia. For identifying very mild dementia vs non-dementia, the AUC was 0.874, with a sensitivity of 0.818, specificity of 0.805, and accuracy of 0.810. Comparable to MMSE and CASI, the 6Q-DS provides a practical, rapid, and user-friendly tool for dementia screening.

Indexed as

DementiaMachine LearningMass ScreeningMental Status and Dementia TestsNeuropsychological TestsAgedAged, 80 and overBoosting Machine Learning AlgorithmsFemaleHumansMaleReproducibility of ResultsSensitivity and SpecificityTaiwancognitive impairmentdementiaearly detectionmachine learningneuropsychological assessment

Identifiers

PMID41701343

DOI10.1177/15333175261424333

PMCPMC12921207

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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.