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Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.

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Title: Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.
Authors: Momota, Yuki (AUTHOR), Liang, Kuo‐ching (AUTHOR), Horigome, Toshiro (AUTHOR), Kitazawa, Momoko (AUTHOR), Eguchi, Yoko (AUTHOR), Takamiya, Akihiro (AUTHOR), Goto, Akiko (AUTHOR), Mimura, Masaru (AUTHOR), Kishimoto, Taishiro (AUTHOR)
Source: Psychiatry & Clinical Neurosciences. May2023, Vol. 77 Issue 5, p273-281. 9p. 1 Diagram, 3 Charts, 1 Graph.
Subjects: Natural language processing, Linguistics, Alzheimer's disease, Japanese people, Machine learning, Bland-Altman plot
Abstract: Aim: The authors applied natural language processing and machine learning to explore the disease‐related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro‐American languages. Methods: The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add‐on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part‐of‐speech and dependency was tagged and counted to create features such as tag‐frequency and tag‐to‐tag transition‐frequency. Each feature's importance was computed during the 100‐fold repeated random subsampling validation and averaged. Results: The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part‐of‐speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words–related features were lower among the patients, whereas those with stagnation‐related features were higher. Conclusion: The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical‐semantic decline known as 'empty speech', which is regarded as a characteristic of AD. [ABSTRACT FROM AUTHOR]
Copyright of Psychiatry & Clinical Neurosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Label: Title
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  Data: Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.
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  Data: <searchLink fieldCode="AR" term="%22Momota%2C+Yuki%22">Momota, Yuki</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liang%2C+Kuo‐ching%22">Liang, Kuo‐ching</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Horigome%2C+Toshiro%22">Horigome, Toshiro</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kitazawa%2C+Momoko%22">Kitazawa, Momoko</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Eguchi%2C+Yoko%22">Eguchi, Yoko</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Takamiya%2C+Akihiro%22">Takamiya, Akihiro</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Goto%2C+Akiko%22">Goto, Akiko</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mimura%2C+Masaru%22">Mimura, Masaru</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kishimoto%2C+Taishiro%22">Kishimoto, Taishiro</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Psychiatry+%26+Clinical+Neurosciences%22">Psychiatry & Clinical Neurosciences</searchLink>. May2023, Vol. 77 Issue 5, p273-281. 9p. 1 Diagram, 3 Charts, 1 Graph.
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  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Linguistics%22">Linguistics</searchLink><br /><searchLink fieldCode="DE" term="%22Alzheimer's+disease%22">Alzheimer's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Japanese+people%22">Japanese people</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Bland-Altman+plot%22">Bland-Altman plot</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Aim: The authors applied natural language processing and machine learning to explore the disease‐related language patterns that warrant objective measures for assessing language ability in Japanese patients with Alzheimer disease (AD), while most previous studies have used large publicly available data sets in Euro‐American languages. Methods: The authors obtained 276 speech samples from 42 patients with AD and 52 healthy controls, aged 50 years or older. A natural language processing library for Python was used, spaCy, with an add‐on library, GiNZA, which is a Japanese parser based on Universal Dependencies designed to facilitate multilingual parser development. The authors used eXtreme Gradient Boosting for our classification algorithm. Each unit of part‐of‐speech and dependency was tagged and counted to create features such as tag‐frequency and tag‐to‐tag transition‐frequency. Each feature's importance was computed during the 100‐fold repeated random subsampling validation and averaged. Results: The model resulted in an accuracy of 0.84 (SD = 0.06), and an area under the curve of 0.90 (SD = 0.03). Among the features that were important for such predictions, seven of the top 10 features were related to part‐of‐speech, while the remaining three were related to dependency. A box plot analysis demonstrated that the appearance rates of content words–related features were lower among the patients, whereas those with stagnation‐related features were higher. Conclusion: The current study demonstrated a promising level of accuracy for predicting AD and found the language patterns corresponding to the type of lexical‐semantic decline known as 'empty speech', which is regarded as a characteristic of AD. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Psychiatry & Clinical Neurosciences is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1111/pcn.13526
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        Text: English
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        PageCount: 9
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      – SubjectFull: Natural language processing
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      – SubjectFull: Linguistics
        Type: general
      – SubjectFull: Alzheimer's disease
        Type: general
      – SubjectFull: Japanese people
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      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Bland-Altman plot
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      – TitleFull: Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.
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              M: 05
              Text: May2023
              Type: published
              Y: 2023
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