Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study
Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study
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轻度认知障碍的特征使用泰国版本的联盟建立阿尔茨海默氏病测试注册表：一项多变量和机器学习研究
DOI：
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发表时间：
2018
影响因子：
2.4
通讯作者：
M. Maes
中科院分区：
文献类型：
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作者：
C. Tunvirachaisakul;T. Supasitthumrong;S. Tangwongchai;Solaphat Hemrunroj;P. Chuchuen;Itthipol Tawankanjanachot;Yuthachai Likitchareon;K. Phanthumchinda;S. Sriswasdi;M. Maes
背景资料：建立阿尔茨海默病登记处联盟（CERAD）开发了一种神经心理学成套测试（CERAD-NP）来筛查阿尔茨海默病痴呆患者。轻度认知障碍（MCI）作为痴呆前阶段受到关注。目的：描述MCI的CERAD-NP特征及其临床实用性，以外部验证MCI诊断。方法：采用临床痴呆评定量表对60例MCI患者进行评定，并与63例正常对照组进行比较。数据进行了分析，采用接收器操作特性分析，线性支持向量机，随机森林，自适应增强，神经网络模型，和t-分布随机邻居嵌入（t-SNE）。结果：MCI患者最好区分正常对照组的组合使用的词表回忆，词表记忆，和言语流畅性测试。机器学习表明，从MCI患者和对照组中学习到的CERAD特征并不能很好地预测诊断（最大交叉验证率为77.2%），而t-SNE表明MCI和对照组之间存在相当大的重叠。结论：CERAD-NP区分MCI与正常对照的最重要特征表明情节和语义记忆和回忆的损伤。虽然这些特征显著区分MCI患者和正常对照，但这些测试不能预测MCI。
Background: The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer’s dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. Objectives: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. Methods: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). Results: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. Conclusions: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.