Battista Petronilla, Salvatore Christian, Castiglioni Isabella
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Milano, Italy.
Behav Neurol. 2017;2017:1850909. doi: 10.1155/2017/1850909. Epub 2017 Jan 31.
Subjects with Alzheimer's disease (AD) show loss of cognitive functions and change in behavioral and functional state affecting the quality of their daily life and that of their families and caregivers. A neuropsychological assessment plays a crucial role in detecting such changes from normal conditions. However, despite the existence of clinical measures that are used to classify and diagnose AD, a large amount of subjectivity continues to exist. Our aim was to assess the potential of machine learning in quantifying this process and optimizing or even reducing the amount of neuropsychological tests used to classify AD patients, also at an early stage of impairment. We investigated the role of twelve state-of-the-art neuropsychological tests in the automatic classification of subjects with none, mild, or severe impairment as measured by the clinical dementia rating (CDR). Data were obtained from the ADNI database. In the groups of measures used as features, we included measures of both cognitive domains and subdomains. Our findings show that some tests are more frequently best predictors for the automatic classification, namely, LM, ADAS-Cog, AVLT, and FAQ, with a major role of the ADAS-Cog measures of delayed and immediate memory and the FAQ measure of financial competency.
患有阿尔茨海默病（AD）的受试者会出现认知功能丧失以及行为和功能状态的改变，这会影响他们自己以及家人和护理人员的日常生活质量。神经心理学评估在检测这些与正常情况的变化方面起着至关重要的作用。然而，尽管存在用于对AD进行分类和诊断的临床测量方法，但大量的主观性仍然存在。我们的目的是评估机器学习在量化这一过程以及优化甚至减少用于对AD患者进行分类的神经心理学测试数量方面的潜力，即使在损伤的早期阶段也是如此。我们研究了十二种最先进的神经心理学测试在根据临床痴呆评定量表（CDR）测量的无损伤、轻度损伤或重度损伤受试者的自动分类中的作用。数据来自ADNI数据库。在用作特征的测量组中，我们纳入了认知领域和子领域的测量。我们的研究结果表明，一些测试更经常是自动分类的最佳预测指标，即语言记忆（LM）、阿尔茨海默病协作研究认知量表（ADAS-Cog）、听觉词语学习测验（AVLT）和功能活动问卷（FAQ），其中ADAS-Cog的延迟和即时记忆测量以及FAQ的财务能力测量起主要作用。
