A Hierarchical Neural Network Model for Japanese Toward Detecting Mild Cognitive Impairment
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- Goto Tetsuji
- Faculty of Health and Medical Science, Teikyo Heisei University
Bibliographic Information
- Other Title
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- 軽度認知障害のスクリーニングに向けた階層型ニューラルネットワークモデルの提案
Abstract
<p>We found that some signs of Mild Cognitive Impairment (MCI) might be presented in a structure of a sentence and a relation between sentences talked by a man, and develop a neural network model which has an analogy with the hierarchical structure of speakers, tpoics, sentences and words in Japanese. We build our model based on 2-layered bi-directional LSTM, corresponding to words-sentences and sentences-topics hierarchy. As a layer corresponding to speakers, we use a linear classifier with self-attention. The test result shows a largely improved AUC, compared with our another test by using the normal 2-layered bi-directional LSTM with TBPTT. The result also indicates that there are some characteristic patterns in a talk by an elderly person with MCI. We classify the character vectors of topics generated from our model through learning into clusters whose number is 1/10 of the number of persons in our data. Since these clusters have almost less than 10% or more than 90% rate of positive share, we conclude that we can develop a screening method based on a talk in Japanese by an elderly person in the near future.</p>
Journal
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- IEEJ Transactions on Electronics, Information and Systems
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IEEJ Transactions on Electronics, Information and Systems 143 (4), 465-470, 2023-04-01
The Institute of Electrical Engineers of Japan
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Keywords
Details 詳細情報について
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- CRID
- 1390014183322936064
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- ISSN
- 13488155
- 03854221
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed