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

Abstract

Idea 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. The 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. The 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. The 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.