ArticleAlzheimer's & dementia (New York, N. Y.)2019
Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer's disease status.
Article in Alzheimer's & dementia (New York, N. Y.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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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.
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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.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Machine learning models identify predictive features of patient mortality across dementia types.Communications medicine · 2024Article
- Article
- Predictive Models and Features of Patient Mortality across Dementia Types.Research square · 2023Article
- Midlife Neuropsychological Profiles and Associated Vascular Risk: The Bogalusa Heart Study.Journal of Alzheimer's disease : JAD · 2023Article
- Machine learning for modeling the progression of Alzheimer disease dementia using clinical data: a systematic literature review.JAMIA open · 2021Review
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
introductionSubtle cognitive alterations that precede clinical evidence of cognitive impairment may help predict the progression to Alzheimer's disease (AD). Neuropsychological (NP) testing is an attractive modality for screening early evidence of AD.
methodsLongitudinal NP and demographic data from the Framingham Heart Study (FHS; N = 1696) and the National Alzheimer's Coordinating Center (NACC; N = 689) were analyzed using an unsupervised machine learning framework. Features, including age, logical memory-immediate and delayed recall, visual reproduction-immediate and delayed recall, the Boston naming tests, and Trails B, were identified using feature selection, and processed further to predict the risk of development of AD.
resultsOur model yielded 83.07 ± 3.52% accuracy in FHS and 87.57 ± 1.19% accuracy in NACC, 80.52 ± 3.93%, 86.74 ± 1.63% sensitivity in FHS and NACC respectively, and 85.63 ± 4.71%, 88.41 ± 1.38% specificity in FHS and NACC, respectively. DISCUSSION: Our results suggest that a subset of NP tests, when analyzed using unsupervised machine learning, may help distinguish between high- and low-risk individuals in the context of subsequent development of AD within 5 years. This approach could be a viable option for early AD screening in clinical practice and clinical trials.
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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.