Detecting Japanese Patients with Alzheimer's Disease based on Word Category Frequencies

摘要

In recent years, detecting Alzheimer's disease (AD) in early stages based on natural language processing (NLP) has drawn much attention. To date, vocabulary size, grammatical complexity, and fluency have been studied using NLP metrics. However, the content analysis of AD narratives is still unreachable for NLP. This study investigates features of the words that AD patients use in their spoken language. After recruiting 18 examinees of 53-90 years old (mean: 76.89), they were divided into two groups based on Mini Mental State Examination (MMSE) scores. The AD group comprised 9 examinees with scores of 21 or lower. The healthy control group comprised 9 examinees with scores of 22 or higher. Linguistic Inquiry and Word Count (LIWC). The word frequency was found from observation. Significant differences were confirmed for the usage of impersonal pronouns in the AD group. This result demonstrated the basic feasibility of the proposed NLP-based Alzheimer's disease detection approach.