ArticleJournal of Alzheimer's disease : JAD2021
Screening for Early-Stage Alzheimer's Disease Using Optimized Feature Sets and Machine Learning.
Article in Journal of Alzheimer's disease : JAD, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- Spatial navigation as a digital marker for clinically differentiating cognitive impairment severity.Communications medicine · 2026Article
- How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders.Journal of neuropsychology · 2026Review
- Identifying dementia neuropathology using low-burden clinical data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Reliability and validity analysis of the Chinese version of the Quick Dementia Rating System (QDRS).BMC geriatrics · 2025Article
- A preliminary study of the reliability and validity of the Uyghur version of the NUCOG cognitive screening application.BMC neurology · 2025Article
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- Research on Alzheimer's Disease (AD) Involving the Use ofCentral nervous system agents in medicinal chemistry · 2025Review
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- Integrating convolutional neural networks with ensemble methods for enhanced diabetes diagnosis: a multi-dataset evaluation.Frontiers in medicine · 2025Article
- High frequency post-pause word choices and task-dependent speech behavior characterize connected speech in individuals with mild cognitive impairment.medRxiv : the preprint server for health sciences · 2024Article
- Detection of Alzheimer's Disease Using Logistic Regression and Clock Drawing Errors.Brain sciences · 2023Article
- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer's Disease and Related Dementias.Journal of Alzheimer's disease : JAD · 2023Article
- Article
- A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer's Disease.Brain informatics · 2022Article
- Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.Journal of personalized medicine · 2022Article
- Alzheimer's Disease Assessments Optimized for Diagnostic Accuracy and Administration Time.IEEE journal of translational engineering in health and medicine · 2022Article
- The Brain Health Platform: Combining Resilience, Vulnerability, and Performance to Assess Brain Health and Risk of Alzheimer's Disease and Related Disorders.Journal of Alzheimer's disease : JAD · 2022Article
- Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundDetecting early-stage Alzheimer's disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of undetected dementia.
objectiveWe aim to identify groups of cognitive assessment features optimized for detecting mild impairment that may be used to improve routine screening. We also compare the efficacy of classifying impairment using either a two-class (impaired versus non-impaired) or three-class using the Clinical Dementia Rating (CDR 0 versus CDR 0.5 versus CDR 1) approach.
methodsSupervised feature selection methods generated groups of cognitive measurements targeting impairment defined at CDR 0.5 and above. Random forest classifiers then generated predictions of impairment for each group using highly stochastic cross-validation, with group outputs examined using general linear models.
resultsThe strategy of combining impairment levels for two-class classification resulted in significantly higher sensitivities and negative predictive values, two metrics useful in clinical screening, compared to the three-class approach. Four features (delayed WAIS Logical Memory, trail-making, patient and informant memory questions), totaling about 15 minutes of testing time (∼30 minutes with delay), enabled classification sensitivity of 94.53% (88.43% positive predictive value, PPV). The addition of four more features significantly increased sensitivity to 95.18% (88.77% PPV) when added to the model as a second classifier.
conclusionThe high detection rate paired with the minimal assessment time of the four identified features may act as an effective starting point for developing screening protocols targeting cognitive impairment defined at CDR 0.5 and above.
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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 Socratic graph.