Automatic grouping and text data augmentation about behavioral and psychological symptoms of dementia in Ninchisho Chienowa-net.

In this paper, we describe our research on the possibility of automatically grouping textual information on behavioral and psychological symptoms of dementia stored in the Ninchisho Chienowa-net using the latest deep learning models for natural language processing. Ninchisho Chienowa-net is a web system that publishes the probability that coping methods for various symptoms occurring in patients with dementia will “go well” (probability of success) by collecting care information in the form of the posting from dementia caregivers and by grouping the posted care information according to the similarity of the care information. This grouping has been done manually by clinicians, but since the workload of clinicians has been increasing, we will verify the possibility of automating this process using deep learning models through experiments and investigate practical feasibility in Ninchisho Chienowa-net web system. As a result of the experiment, we constructed a learning model for automatic grouping with 20 epochs after multiple types of data preprocessing, and confirmed that the model can output the correct group within the top five with a probability of more than 90%. Furthermore, for the purpose of improving the performance of the automatic grouping model, we conducted data augmentation of text data using a translation service, and confirmed that we could output the correct groups within the top five with a probability of 98%.