Linguistic-Based Mild Cognitive Impairment Detection Using Informative
  Loss
Ali Pourramezan Fard Mohammad H. Mahoor Muath Alsuhaibani Hiroko H. Dodgec
This paper presents a deep learning method using Natural Language Processing
(NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and
Normal Cognitive (NC) conditions in older adults. We propose a framework that
analyzes transcripts generated from video interviews collected within the
I-CONECT study project, a randomized controlled trial aimed at improving
cognitive functions through video chats. Our proposed NLP framework consists of
two Transformer-based modules, namely Sentence Embedding (SE) and Sentence
Cross Attention (SCA). First, the SE module captures contextual relationships
between words within each sentence. Subsequently, the SCA module extracts
temporal features from a sequence of sentences. This feature is then used by a
Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC.
To build a robust model, we propose a novel loss function, called InfoLoss,
that considers the reduction in entropy by observing each sequence of sentences
to ultimately enhance the classification accuracy. The results of our
comprehensive model evaluation using the I-CONECT dataset show that our
framework can distinguish between MCI and NC with an average area under the
curve of 84.75%.