A Transfer Learning Method for Detecting Alzheimer's Disease Based on Speech and Natural Language Processing

Frontiers in Public Health

article

Ning Liu¹  Kexue Luo³  Zhenming Yuan⁴  Yan Chen⁵ 

[1] School of Public Health, Hangzhou Normal University;Department of Mathematics and Computer Science, Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University;Tongde Hospital of Zhejiang Province Geriatrics;School of Information Science and Technology, Hangzhou Normal University;International Unresponsive Wakefulness Syndrome and Consciousness Science Institute, Hangzhou Normal University

关键词: transfer learning;    Alzheimer's disease;    natural language processing;    BERT;    machine learning;   

DOI  :  10.3389/fpubh.2022.772592

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Alzheimer's disease (AD) is a neurodegenerative disease that is difficult to be detected using convenient and reliable methods. The language change in patients with AD is an important signal of their cognitive status, which potentially helps in early diagnosis. In this study, we developed a transfer learning model based on speech and natural language processing (NLP) technology for the early diagnosis of AD. The lack of large datasets limits the use of complex neural network models without feature engineering, while transfer learning can effectively solve this problem. The transfer learning model is firstly pre-trained on large text datasets to get the pre-trained language model, and then, based on such a model, an AD classification model is performed on small training sets. Concretely, a distilled bidirectional encoder representation (distilBert) embedding, combined with a logistic regression classifier, is used to distinguish AD from normal controls. The model experiment was evaluated on Alzheimer's dementia recognition through spontaneous speech datasets in 2020, including the balanced 78 healthy controls (HC) and 78 patients with AD. The accuracy of the proposed model is 0.88, which is almost equivalent to the champion score in the challenge and a considerable improvement over the baseline of 75% established by organizers of the challenge. As a result, the transfer learning method in this study improves AD prediction, which does not only reduces the need for feature engineering but also addresses the lack of sufficiently large datasets.

【 授权许可】

CC BY