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Linguistic features and automatic classifiers for identifying mild cognitive impairment and dementia

机译：识别轻度认知障碍和痴呆症的语言特征和自动分类器

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Almost 50 million people are living with dementia in 2018 worldwide, and the number will double every 20 years. The effectiveness of existing pharmacologic treatments for the disease is limited to symptoms control, and none of them are able to prevent, reverse or turn off the neurodegenerative process that leads to dementia; therefore, a prompt detection of the "disease signature" is a key problem, in order to develop and test new drugs and to support the management of clinical and domestic context. Recent studies showed that linguistic alterations may be one of the earliest signs of the pathology, years before other neurocognitive deficits become evident. Traditional tests fail to identify these slight but noticeable changes; whereas, the analysis of spoken language productions by Natural Language Processing (NIP) techniques can ecologically and inexpensively identify minor language modifications in potential patients. This interdisciplinary study aims at quantifying and describing alterations of linguistic features due to cognitive decline and build an automatic system for early diagnosis and screening purpose. To this aim, we enrolled 96 participants: 48 healthy controls and 48 impaired subjects. Of the latter, 32 was diagnosed with Mild Cognitive Impairment and 16 with early Dementia (eD). Each subject underwent a brief neuropsychological screening, and samples of semi-spontaneous speech productions was collected by means of three elicitation tasks. Recorded sessions were orthographically transcribed, PoS tagged and parsed building two different corpora: in the first we kept the automatic annotations, while in the second the transcripts were manually corrected in order to remove all mistakes. A multidimensional parameter computation was performed on the data, taking into consideration a set of 87 acoustical, rhythmical, morpho-syntactic and lexical feature as well as some readability indexes and demographic information. After these preparatory steps, some automatic classifiers were trained to distinguish healthy controls from MCI subjects employing two different algorithms, Support Vector (SVC) and Random Forest Classifiers (RFC). Our system was able to distinguish between controls and MCI subjects exhibiting high F1 scores, around 75%, thus it seems to be a promising approach for the identification of preclinical stages of dementia.
机译：2018年，近5000万人在2018年患有痴呆症，并且数字每20年都会增加一次。对疾病现有药理治疗的有效性仅限于症状控制，并且它们均不能防止，逆转或关闭导致痴呆症的神经变性过程;因此，迅速检测“疾病签名”是关键问题，以便开发和测试新药并支持临床和国内背景的管理。最近的研究表明，语言改变可能是本病理的最早迹象之一，在其他神经认知缺陷变得明显之前。传统测试未能识别这些轻微但明显的变化;然而，通过自然语言处理（NIP）技术对语言制作的分析可以生态且廉价地识别潜在患者的轻微语言修改。这种跨学科研究旨在量化和描述由于认知下降和建立早期诊断和筛选目的的自动系统，这些研究旨在量化和描述语言特征的改变。为此目的，我们注册了96名参与者：48个健康控制和48名受损的科目。后者，32例被诊断为轻度认知障碍和16例，早期痴呆（ED）。每个受试者经历了短暂的神经心理学筛选，并通过三种诱导任务收集半自发性言语制作的样本。录制的会话在正面转录，POS标记并解析了两个不同的基层：在第一个我们保持自动注释，而在第二个中，在第二个中，手动纠正了成绩单，以便删除所有错误。考虑到一组87个声学，有节奏，形态语法和词法特征以及一些可读性指标和人口统计信息，对数据进行多维参数计算。在这些预备步骤之后，培训了一些自动分类器以区分使用两种不同算法，支持载体（SVC）和随机林分类器（RFC）的MCI受试者的健康对照。我们的系统能够区分表现出高F1分数的控制和MCI受试者约为75％，因此似乎是鉴定痴呆症的临床前阶段的有希望的方法。

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