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KOTOBAKARI Study: Using Natural Language Processing of Patient Short Narratives to Detect Cancer Related Cognitive
Eiji Aramaki1, Mai Miyabe2, Chihiro Honda1
1Nara Institute of Science and Technology (NAIST), Nara, Japan.
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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