ArticleScientific reports2022
Identifying neurocognitive disorder using vector representation of free conversation.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A 12-week application-based group conversation intervention on cognitive health and psychosocial well-being among older adults during the COVID-19 pandemic: a randomized controlled trial.BMC geriatrics · 2025Trial
- A Systematic Review of Natural Language Processing Techniques for Early Detection of Cognitive Impairment.Mayo Clinic proceedings. Digital health · 2025Review
- Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses.Diagnostics (Basel, Switzerland) · 2025Review
- Using voice recognition and machine learning techniques for detecting patient-reported outcomes from conversational voice in palliative care patients.Japan journal of nursing science : JJNS · 2025Article
- Article
- Applications of artificial intelligence in dementia.Geriatrics & gerontology international · 2024Review
- Comparing global and local semantic coherence of spontaneous speech in persons with Alzheimer's disease and healthy controls.Applied Corpus Linguistics · 2023Article
- Diagnosing psychiatric disorders from history of present illness using a large-scale linguistic model.Psychiatry and clinical neurosciences · 2023Article
- Novel Screening Tool Using Non-linguistic Voice Features Derived from Simple Phrases to Detect Mild Cognitive Impairment and Dementia.JAR life · 2023Article
- Written discourse in diagnosis for acquired neurogenic communication disorders: current evidence and future directions.Frontiers in human neuroscience · 2023Review
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 natural sentences into morphemes by performing a morphological analysis using NLP, and then converted into real-valued vectors that were used as features for machine learning. A total of 432 datasets were used, and the resulting machine learning model classified the data for dementia and non-dementia subjects with an accuracy of 0.900, sensitivity of 0.881, and a specificity of 0.916. Using sentence vector information, it was possible to develop a machine-learning algorithm capable of discriminating dementia from non-dementia subjects with a high accuracy based on free conversation.
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