@inproceedings{duan-etal-2023-cda,
    title = "{CDA}: A Contrastive Data Augmentation Method for {A}lzheimer{'}s Disease Detection",
    author = "Duan, Junwen  and
      Wei, Fangyuan  and
      Liu, Jin  and
      Li, Hongdong  and
      Liu, Tianming  and
      Wang, Jianxin",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-acl.114",
    doi = "10.18653/v1/2023.findings-acl.114",
    pages = "1819--1826",
    abstract = "Alzheimer{'}s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient{'}s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.",
}

Alzheimer{'}s Disease (AD) is a neurodegenerative disorder that significantly impacts a patient{'}s ability to communicate and organize language. Traditional methods for detecting AD, such as physical screening or neurological testing, can be challenging and time-consuming. Recent research has explored the use of deep learning techniques to distinguish AD patients from non-AD patients by analysing the spontaneous speech. These models, however, are limited by the availability of data. To address this, we propose a novel contrastive data augmentation method, which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. The corrupted samples are expected to be in worse conditions than the original by a margin. Experimental results on the benchmark ADReSS Challenge dataset demonstrate that our model achieves the best performance among language-based models.
Duan, Junwen Wei, Fangyuan Liu, Jin Li, Hongdong Liu, Tianming Wang, Jianxin
CDA: A Contrastive Data Augmentation Method for Alzheimer's Disease Detection