Idea density in Japanese for the early detection of dementia based on narrative speech

BackgroundIdea density (ID), a natural language processing–based index, was developed to aid in the detection of dementia through the analysis of English narratives. However, it has not been applied to non-English languages due to the difficulties in translating grammatical concepts. In this study, we defined rules to count ideas in Japanese narratives based on a previous study and proposed a novel method to estimate ID in Japanese text using machine translation.MaterialsThe study participants comprised 42 Japanese patients with dementia aged 69–98 years (mean: 84.95 years). We collected free narratives from the participants to build a speech corpus. The narratives of the patients were translated into English using three machine translation systems: Google Translate, Bing Translator, and Excite Translator. The ID in the translated text was then calculated using the Dependency-based Propositional ID (DEPID), an English ID scoring tool.ResultsThe maximum correlation coefficient between ID calculated using DEPID-R-ADD (a modified DEPID method to calculate ID after removing vague sentences) and the Mini-Mental State Examination score was 0.473, indicating a moderate correlation.DiscussionThe results demonstrate the feasibility of machine translation-based ID measurement. We believe that the basic concept of this translation approach can be applied to other non-English languages.

研究背景
意念密度（Idea Density, ID）是一种基于自然语言处理的指标，最初通过分析英文叙事文本，用于辅助痴呆症的检测。然而，受限于语法概念翻译的诸多难点，该指标尚未在非英语语言中得到应用。本研究基于既往研究成果，制定了日语叙事文本的意念计数规则，并提出了一种依托机器翻译估算日语文本ID的全新方法。

研究材料
本研究的研究对象为42名日本痴呆症患者，年龄区间为69至98岁，平均年龄84.95岁。研究人员采集了受试者的自由叙事文本，以构建语音语料库。受试者的叙事文本通过三款机器翻译系统——谷歌翻译（Google Translate）、必应翻译（Bing Translator）与Excite翻译（Excite Translator）——译为英文。随后，采用基于依存句法的命题意念密度评分工具（Dependency-based Propositional ID, DEPID）——一款英文ID评分工具——计算译文中的ID值。

研究结果
采用DEPID-R-ADD（一种改进型DEPID方法，通过移除模糊语句后计算ID）得到的ID值与简易精神状态检查表（Mini-Mental State Examination, MMSE）得分之间的最大相关系数为0.473，表明二者存在中等程度的相关性。

讨论
本研究结果证实了基于机器翻译的ID测量方法具备可行性。我们认为，该翻译方案的核心思路可推广至其他非英语语言场景。