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

Daisaku Shibata, Kaoru Ito, Hiroyuki Nagai, Taro Okahisa, Ayae Kinoshita, Eiji Aramaki

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Background 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. Materials 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. Results 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. Discussion 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.

Original languageEnglish
Article numbere0208418
JournalPloS one
Issue number12
Publication statusPublished - Dec 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • General


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