CDA : A Contrastive Data Augmentation Method for A lzheimer’s Disease Detection (2023)

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.

## introduction alzheimer's disease (ad) is a debilitating neurodegenerative disorder characterized by a progressive cognitive decline that is currently incurable. it accounts for up to 70% of all cases of dementia @xcite . with an aging population, the prevalence of ad is on the rise. as symptoms of alzheimer's disease can be mistaken for a variety of other cognitive disorders, traditional diagnostic methods, such as physical screening or neurological testing, can be challenging and time-consuming. furthermore, they require a certain degree of clinician expertise @xcite . consequently, the development of automatic detection methods for alzheimer's disease is essential to the advancement of current medical treatment. the use of machine learning methods to detect ad or other diseases automatically has gained increasing attention in recent years @xcite @xcite . nevertheless, these approaches have limitations due to a lack of data and the generalizability of the models. some studies have attempted to address this problem by model ensembling @xcite , multi-task learning @xcite or data augmentation @xcite , but the improvement in performance is not always substantial. inspired by previous research that ad patients often have language disorders, such as difficulties in word finding and comprehension @xcite , we propose a novel contrastive data augmentation (cda) approach for automatic ad detection. in our study, we simulated cognitive decline associated with alzheimer's disease by randomly deleting words from the speech transcript to create negative samples. it is expected that the corrupted samples are in worse condition than the original due to the degradation of coherence and semantic integrity. compared to traditional data augmentation methods, the cda method expands the dataset scale and utilizes augmented data more effectively. we have demonstrated in our experiments on the adress challenge dataset that our approach uses linguistic features alone, is more generalizable to unseen data, and achieves superior results compared to strong baselines. ## data and preprocessing we use the data from the adress challenge (alzheimer's dementia recognition through spontaneous speech) @xcite , a subset of the dementiabank's english pitt corpus @xcite . it consists of recordings and transcripts of spoken picture descriptions from the boston diagnostic aphasia examination. during the examination, the subject is shown a picture and is asked to describe its content in their own language. ## methods figure 1 illustrates the framework of the proposed model. firstly, for each transcript, we generate a number of augmented instances, which are then input to text encoder along with the original transcripts to obtain their corresponding representations. then the classifier uses feature vectors acquired in text encoder and output a probability of being ad for each transcript and its corresponding augmented samples. we will discuss more details in the following subsections. ## experiments we employ 10-fold cross-validation to estimate the generalization error and adjust the model's parameter settings. the best setting is used to retrain models on the whole train set with five different random seeds and is then applied to the test set. the results reported in this paper are the average of these models. the accuracy is used as the primary metric of task performance since the dataset is balanced. recall, precision, and f1 are also reported for the ad class to provide a more comprehensive assessment. the hyperparameters in our model are: learning @xmath0, batch @xmath1, @xmath2, n @xmath3, n @xmath4, @xmath5.3, @xmath6.1. ## conclusion our experiments show the potential of contrastive data argumentation in improving the accuracy of models for alzheimer's disease diagnosis. as a comparison to large, complex multimodal models, and other techniques of data augmentation, we obtain the best results by simulating cognitive impairment caused by ad. despite the small size of the dataset, the results of this study provide a basis for further research into more complex issues. acl 2023 responsible nlp checklist a for every submission: a1. did you describe the limitations of your work? section limitaion. a2. did you discuss any potential risks of your work? section ethics statement. a3. do the abstract and introduction summarize the paper's main claims? section 1. a4. have you used ai writing assistants when working on this paper? left blank. b did you use or create scientific artifacts? section 2. b1. did you cite the creators of artifacts you used? section 2. b2. did you discuss the license or terms for use and / or distribution of any artifacts? section ethics statement. b3. did you discuss if your use of existing artifact(s) was consistent with their intended use, provided that it was specified? for the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? section ethics statement. b4. did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? 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