Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease.
Latent information in fluency lists predicts functional decline in persons at risk for Alzheimer disease.
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DOI：
10.1016/j.cortex.2013.12.013
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发表时间：
2014-06
期刊：
影响因子：
3.6
通讯作者：
Marson, D. C.
中科院分区：
文献类型：
--
作者：
Clark, D. G.;Kapur, P.;Geldmacher, D. S.;Brockington, J. C.;Harrell, L.;DeRamus, T. P.;Blanton, P. D.;Lokken, K.;Nicholas, A. P.;Marson, D. C.
关键词：
我们采用传统的语义流畅度评分方法和新方法构建了随机森林分类器。然后比较这些分类器在诊断阿尔茨海默病（AD）的能力，或在从认知正常（CN）到轻度认知障碍（MCI）到AD的个体中进行预后的能力。将44例认知正常老年人、80例轻度认知障碍患者和41例AD患者的语义流畅性列表转录成电子文本文件，采用传统原始评分、聚类和转换评分、“广义”聚类和转换评分和基于独立成分分析（ICA）的方法进行评分。将基于原始分数的随机森林分类器与采用较新评分方法的“增强”分类器进行比较。结果变量包括基线AD诊断、MCI转换、临床痴呆评分-盒和（CDR-SOB）评分增加或财务能力工具（FCI）评分下降。为每个分类器构建ROC曲线，并计算曲线下面积（AUC）。我们使用Delong’s检验比较了原始分类器和增强分类器的AUC，并评估了增强分类器的效度和信度。增强分类器在结果测量方面优于基于原始分数的分类器，如AD诊断（AUC 0.97 vs. 0.95）、MCI转换（AUC 0.91 vs. 0.77）、CDR-SOB增加（AUC 0.90 vs. 0.79）和FCI下降（AUC 0.89 vs. 0.72）。随着时间的推移，有效性和稳定性的度量支持该方法的使用。语义流畅性词汇表中的潜在信息可用于预测老年AD高危人群的认知和功能衰退。现代机器学习方法可以结合潜在信息来提高语义流畅性原始分数的诊断价值。这些方法可以产生对病人护理和临床试验设计有价值的信息，而花费的时间和金钱相对较少。
We constructed random forest classifiers employing either the traditional method of scoring semantic fluency word lists or new methods. These classifiers were then compared in terms of their ability to diagnose Alzheimer disease (AD) or to prognosticate among individuals along the continuum from cognitively normal (CN) through mild cognitive impairment (MCI) to AD. Semantic fluency lists from 44 cognitively normal elderly individuals, 80 MCI patients, and 41 AD patients were transcribed into electronic text files and scored by four methods: traditional raw scores, clustering and switching scores, “generalized” versions of clustering and switching, and a method based on independent components analysis (ICA). Random forest classifiers based on raw scores were compared to “augmented” classifiers that incorporated newer scoring methods. Outcome variables included AD diagnosis at baseline, MCI conversion, increase in Clinical Dementia Rating-Sum of Boxes (CDR-SOB) score, or decrease in Financial Capacity Instrument (FCI) score. ROC curves were constructed for each classifier and the area under the curve (AUC) was calculated. We compared AUC between raw and augmented classifiers using Delong’s test and assessed validity and reliability of the augmented classifier. Augmented classifiers outperformed classifiers based on raw scores for the outcome measures AD diagnosis (AUC 0.97 vs. 0.95), MCI conversion (AUC 0.91 vs. 0.77), CDR-SOB increase (AUC 0.90 vs. 0.79), and FCI decrease (AUC 0.89 vs. 0.72). Measures of validity and stability over time support the use of the method. Latent information in semantic fluency word lists is useful for predicting cognitive and functional decline among elderly individuals at increased risk for developing AD. Modern machine learning methods may incorporate latent information to enhance the diagnostic value of semantic fluency raw scores. These methods could yield information valuable for patient care and clinical trial design with a relatively small investment of time and money.
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影响因子：
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作者：
Furney, Simon J.;Kronenberg, Deborah;Lovestone, Simon
通讯作者：
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影响因子：
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作者：
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Alzheimer’s Disease Neuroimaging Initiative
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作者：
MCKHANN, G;DRACHMAN, D;STADLAN, EM
通讯作者：
STADLAN, EM