A hierarchical neural network model for Japanese toward detecting mild cognitive impairment

Abstract

AbstractWe 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, topics, 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 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.