Analysis of Hierarchical Multi-Content Text Classification Model on B - SHARP Dataset for Early Detection of A lzheimer’s Disease (2020)

Abstract: This paper presents a new dataset, B-SHARP, that can be used to develop NLP models for the detection of Mild Cognitive Impairment (MCI) known as an early sign of Alzheimer’s disease. Our dataset contains 1-2 min speech segments from 326 human subjects for 3 topics, (1) daily activity, (2) room environment, and (3) picture description, and their transcripts so that a total of 650 speech segments are collected. Given the B-SHARP dataset, several hierarchical text classification models are developed that jointly learn combinatory features across all 3 topics. The best performance of 74.1% is achieved by an ensemble model that adapts 3 types of transformer encoders. To the best of our knowledge, this is the first work that builds deep learning-based text classification models on multiple contents for the detection of MCI.

## introduction alzheimer's disease (ad) is a progressive neurodegenerative disorder that is associated with memory loss and declines in major brain functions including semantic and pragmatic levels of language processing @xcite . traditional cognitive assessments such as positron emission tomography or cerebrospinal fluid analysis are expensive and time-consuming @xcite . this may cause delay in treating ad, known to be irreversible and incurable @xcite , and put an increasing pressure on public health, especially for seniors whose life expectancy is rapidly growing yet are more likely to develop ad. thus, it is crucial to find a more intelligent way of detecting ad in the earliest stage possible @xcite . mild cognitive impairment (mci) is considered the first phase that patients start having biomarker evidence of brain changes that can eventually lead to ad @xcite . mci involves subtle language changes from impairment in reasoning that may not be noticeable to people other than friends and relatives. because of this, the detection of mci is a much more challenging task than detecting dementia @xcite . recent studies in nlp have shown that it is possible to detect early stages of ad by analyzing patients' language patterns; however, most previous works have focused on the detection of dementia instead and researches tackling the detection of mci have been based on relatively small datasets (section 2). this paper presents a new dataset that comprises three types of speech segments from both normal controls and mci patients (section 3). then, a hierarchical text classification model is proposed, which jointly learns features from all three types of speech segments to determine whether or not each subject has mci (section 4). individual and ensemble models using three types of transformer encoders are evaluated on our dataset and show that different transformer encoders reveal strengths in distinct types of speeches (section 5). we believe that this work takes the initiative of deep learningbased nlp for detecting mci that will be broadly beneficial to global public health. ## related work only few studies have tackled the detection of mci using nlp. 1 @xcite conducted interviews with (27 c , 14 m ), and developed svm and random forest models on their transcribed speeches. @xcite conducted three speech tasks with (48 c , 32 m , 16 d ), and analyzed phonetic and linguistic features of their speeches and transcripts. @xcite conducted 3 language tasks with (29 c , 26 m ), and built a cascade model to learn multimodal features such as audio, text, eye-tracking. @xcite conducted question answer- all of the previous works were based on fewer than 100 subjects using traditional linguistic features to develop nlp models, compared to our work that is based on 326 subjects and 650 recordings using the latest transformer-based deep neural models. the task of dementia detection has been more explored by the nlp community. @xcite presented the dementiabank, that consists of 552 audio recordings describing the picture called "the boston cookie theft" from 99 normal controls and 194 dementia patients, that have been used by the following works. @xcite presented deep-deep neural network language models using higher-order n-grams and skip-grams. pou-prom and rudzicz (2018) leveraged linguistic features and multiview embeddings by applying generalized canonical correlation analysis. @xcite proposed a model based on convolutional and recurrent neural networks and gave interpretations of this model to explain linguistic characteristics for detecting dementia. our work is distinguished as: ## hierarchical transformer although transformer encoders have recently established the state-of-the-art results on most document classification tasks, they have a limit on the input size. as in let w @xmath1, . . . , w in } be a transcript, where w ij represents the j'th token in the transcript produced by the i'th task q i (in our case, @xmath2, 2, 3}). w i is prepended by the special token [cls i ] that is used to learn the transcript embedding, and fed into the transformer t i . the transformer then generates e @xmath3, e i1 , . . . , e in }, where c i and e ij are the embeddings for [cls i ] and w ij , respectively. c @xmath4. first, c i is fed into a multilayer perceptron layer, mlp i , that generates the output vector o @xmath5. second, the transcript embeddings from all three tasks are concatenated such that c @xmath6, which gets fed into another mlp e to generate the output vector o @xmath7, and makes the binary decision based on the transcripts from all three tasks, q 1 , q 2 and q 3 . ## experiments there are 650 recordings in our dataset ( it is worth mentioning that all recordings from the same subject given multiple visits are assigned to the same cv set; thus, there is no overlap in terms of subjects across these cv sets. this allows us to avoid potential inflation in accuracy due to unique language patterns used by individual subjects. ## conclusion this paper presents the b-sharp dataset, that is the largest dataset for the task of mci detection feasible to develop robust deep neural models. our best ensemble model using hierarchical transformer gives the accuracy of 74% to distinguish mci patients from normal controls that is very promising. we will also explore models to make a longevity analysis per patient with this dataset. 8