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Updated: Sep 12, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Large Language Model Challenges to Detect Cancer-Related Cognitive Impairment from Patient Short Speech
Sofia Gaiduchenko1, Eiji Aramaki1, Seiji Shimidzu1
1Nara Institute of Science and Technology (NAIST), Nara, Japan.
Abstract:
This study uses language-based Cancer-Related Cognitive Impairment (CRCI) screening to examine the language ability of the participants. This study was conducted to determine whether a natural language processing-based system can detect CRCI or not. We obtained speech samples from participants including patients with cancer and cognitive impairment scores. Using LLMs, we extracted 8 linguistic measurement metrics from the collected data. We divided patients into high-cognitive and low-cognitive) groups. The results did not show any correlation between CRCI and language features derived from participants' speech using LLMs.
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