Deep Learning-Based Dementia Prediction Using Multimodal Data

Abstract

In this project we propose a deep learning architecture to predict dementia, a disease which affects around 55 million people all over the world and makes them in some cases dependent people. The main aim is to predict the disease in the early stages, in order to start having professional treatment from the beginning, which can improve the quality of life of the patients. Another aim is to analyze how the combination of different modalities, like audio or text, can influence the results obtained by the model. Many research has been done over the different available dementia datasets as well as the classification tasks with audio and text data. To this end, we have used the DementiaBank dataset, which includes audio recordings as well as their transcriptions of healthy people and people with dementia. Different models have been used and tested, including Convolutional Neural Networks for the audio classification, Transformers for the text classification and a combination of both models in a multimodal one. These models have been tested over a test set, obtaining the best results from the text modality, achieving a 90.36% of accuracy on the detection of dementia task.

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

“…Without sufficient data for training, deep learning models may struggle to generalize effectively across different types and severities of aphasia, leading to reduced performance and reliability in real-world applications [22,26]. Improving access to annotated datasets and developing techniques for efficient data annotation are essential for advancing research in this area [15,16,[27][28][29][30][31][32][33][34].…”

“…Ensuring that deep learning solutions are user-friendly and seamlessly integrated into clinical workflows is essential for their adoption and effectiveness in real-world settings [22,29]. Clinicians may have varying levels of technical expertise and familiarity with technology, so designing intuitive interfaces and workflows that align with existing clinical practices is critical for facilitating adoption and usability [16].…”

“…Close collaboration with domain experts can ensure that annotated datasets accurately reflect the linguistic characteristics and diagnostic criteria relevant to aphasia, enhancing the quality and clinical utility of deep learning models trained on such data. Collaborating with speech-language pathologists ensures accurate annotation of aphasic speech data, addressing the challenge of obtaining accurately annotated datasets [29]. By involving domain experts in the annotation process, researchers can ensure that deep learning models are trained on high-quality data that capture the nuances of aphasic speech patterns effectively.…”