Explainable Identification of Dementia from Transcripts using Transformer Networks

• URL: http://arxiv.org/abs/2109.06980v1
• Date: Tue, 14 Sep 2021 21:49:05 GMT
• Title: Explainable Identification of Dementia from Transcripts using Transformer Networks
• Authors: Loukas Ilias, Dimitris Askounis
• Abstract summary: Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. We introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification) and the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification) Our model obtains accuracy equal to 84.99% on the detection of AD patients in the multi-task learning setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Alzheimer's disease (AD) is the main cause of dementia which is accompanied by loss of memory and may lead to severe consequences in peoples' everyday life if not diagnosed on time. Very few works have exploited transformer-based networks and despite the high accuracy achieved, little work has been done in terms of model interpretability. In addition, although Mini-Mental State Exam (MMSE) scores are inextricably linked with the identification of dementia, research works face the task of dementia identification and the task of the prediction of MMSE scores as two separate tasks. In order to address these limitations, we employ several transformer-based models, with BERT achieving the highest accuracy accounting for 85.56%. Concurrently, we propose an interpretable method to detect AD patients based on siamese networks reaching accuracy up to 81.18%. Next, we introduce two multi-task learning models, where the main task refers to the identification of dementia (binary classification), while the auxiliary one corresponds to the identification of the severity of dementia (multiclass classification). Our model obtains accuracy equal to 84.99% on the detection of AD patients in the multi-task learning setting. Finally, we present some new methods to identify the linguistic patterns used by AD patients and non-AD ones, including text statistics, vocabulary uniqueness, word usage, correlations via a detailed linguistic analysis, and explainability techniques (LIME). Findings indicate significant differences in language between AD and non-AD patients.

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