Identifying neurocognitive disorder using vector representation of free conversation.

ABSTRACT: In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learning effect. Therefore, the purpose of this study was to develop a machine learning model to discriminate subjects with and without dementia by extracting features from unstructured free conversation data using NLP. We recruited patients who visited a specialized outpatient clinic for dementia and healthy volunteers. Participants' conversation was transcribed and the text data was decomposed from na

SUBMITTER: Horigome T 

PROVIDER: S-EPMC9349220 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature