Predicting mild cognitive impairment from spontaneous spoken utterances.
Predicting mild cognitive impairment from spontaneous spoken utterances.
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DOI：
10.1016/j.trci.2017.01.006
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发表时间：
2017-06
期刊：
影响因子：
--
通讯作者：
Dodge H
中科院分区：
文献类型：
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作者：
Asgari M;Kaye J;Dodge H
阿尔茨海默病的试验越来越关注无症状个体的预防。我们假设轻度认知障碍（MCI）的指标可能存在于老年人的口语内容中，并且有助于区分患有MCI的人和认知完好的人。为了验证这一假设，我们对参与临床试验的MCI患者和认知完好的参与者的口语进行了语言分析。
数据来自一项随机对照行为临床试验，该试验旨在研究无结构对话对认知正常或患有MCI的老年人认知功能的影响（ClinicalTrials.gov：NCT01571427）。在试验的干预阶段，从14名MCI患者和27名认知完好的参与者中，记录了访谈者和被访谈者之间的无结构对话（但在受试者之间有标准化的预选话题）。从被访谈者录音的转录文本中，我们使用语言查询与字数统计（LIWC）对口语词汇进行分组，LIWC是一个结构化的词汇表，它将2500个单词分为68个不同的单词子类，如积极和消极词汇、填充词和身体状态词汇。每个LIWC单词子类中的单词数量构成了一个68维的向量，代表每个受试者的语言特征。我们使用支持向量机和随机森林分类器来区分MCI患者和认知完好的参与者。
使用LIWC获得的语言特征区分MCI患者和认知完好的参与者，分类准确率为84%，远高于60%的随机概率。
口语的语言分析可能是区分MCI患者和认知完好者的有力工具。有必要进一步研究以评估源自口语的测量指标是否能够在临床试验中检测认知功能的变化。
Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial. Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI (ClinicalTrials.gov: NCT01571427). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants. MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%. Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.