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KOTOBAKARI Study: Using Natural Language Processing of Patient Short Narratives to Detect Cancer Related Cognitive Impairment
Eiji Aramaki, Mai Miyabe, Chihiro Honda, Seiko Isozaki, Shoko Wakamiya, Akira Sato, Isao Miyashiro
1111 - 1115
10.3233/SHTI190398
Research Article
Studies in Health Technology and Informatics
Volume 264: MEDINFO 2019: Health and Wellbeing e-Networks for All
Abstract

Background:

Recent reports of some studies have described that the cognitive function of cancer patients often declines by a phenomenon designated as cancer related cognitive impairment (CRCI). For patients’ decision-making, detecting CRCI is important. To do so, this study uses language-based CRCI screening to examine participants’ language ability.

Objective:

This study was conducted to ascertain whether a Natural Language Processing (NLP) based system can detect CRCI, or not.

Materials and Methods:

We obtained materials of two types from cancer patients (n = 116): (1) speech samples on three topics, and (2) cognitive function level test scores from Hasegawa’s Dementia Scale – Revised (HDS-R), a test used in Japan for dementia patients. The test is similar to the Mini-Mental State Examination.

Results and Discussion:

Cancer patients with lower HDS-R scores showed a significantly lower Type Token Ratio (TTR).

Conclusion:

This result demonstrates the feasibility of the proposed speech–language-based CRCI screening method.

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