Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2104-2109. doi: 10.1109/EMBC46164.2021.9629861.
Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) are among the most common health conditions in elderly patients. Currently, methods to diagnose AD and MCI are lengthy, costly and require specialized staff to operate. A picture description task was developed to speed up the diagnosis. It was designed to be suitable and relatable to the Thai culture. In this paper, we will be presenting two picture description tasks named Thais-at-Home and Thai Temple Fair. The developed picture set was presented to 90 participants (30 normals, 30 MCI patients, and 30 AD patients). Then, the recording in the form of spontaneous speech is converted to text. A Part-of-Speech (PoS) tagger is used to categorize words into 7 types (noun, pronoun, adjective, verb, conjunction, preposition, and interjection) according to the Office of the Royal Society of Thailand. Six machine learning algorithms were applied to train with the PoS patterns and their performances were compared. Results showed that the PoS can be used to classify patients (MCI and AD) and healthy controls using multilayer perceptron with 90.00% sensitivity, 80.00% specificity, and 86.67% accuracy. Moreover, the findings showed that healthy controls used more conjunctions and verbs but fewer pronouns than the patients.Clinical relevance- The picture description tasks using part-of-speech (PoS) to showed promising results in screening Alzheimer's patients.
阿尔茨海默病（AD）和轻度认知障碍（MCI）是老年患者中最常见的健康问题之一。目前，AD 和 MCI 的诊断方法冗长、昂贵，且需要专业人员操作。图片描述任务被开发出来以加速诊断。它被设计为适合并与泰国文化相关。在本文中，我们将介绍两个图片描述任务，分别命名为“泰国家庭”和“泰国寺庙集市”。开发的图片集呈现给了 90 名参与者（30 名正常人、30 名 MCI 患者和 30 名 AD 患者）。然后，以自然语言的形式记录下来的语音被转换成文字。词性标注（PoS）标签器根据泰国皇家学会的分类标准将单词分为 7 类（名词、代词、形容词、动词、连词、介词和感叹词）。我们应用了 6 种机器学习算法来训练 PoS 模式，并比较了它们的性能。结果表明，使用多层感知机可以使用 PoS 对 MCI 和 AD 患者与健康对照组进行分类，其敏感性为 90.00%，特异性为 80.00%，准确性为 86.67%。此外，研究结果表明，健康对照组比患者使用更多的连词和动词，而使用的代词较少。临床意义- 使用词性（PoS）的图片描述任务在筛查阿尔茨海默病患者方面显示出有前景的结果。
