Diagnosis of Dementia using Transformer Models

Sammanfattning: Dementia is a syndrome of illnesses resulting in cognitive decline, severely impacting the lives of those afflicted as well as their loved ones. The most common form of dementia is Alzheimer's disease, with roughly 10 million new cases each year. In this study we examine different machine learning models and approaches aimed to aid healthcare professionals in early diagnosis of Alzheimer's disease, potentially automating parts of the diagnostic process. We evaluate our models on the Pitt corpus of the Dementia Bank dataset, using 10-fold cross validation. We compare the BERT and RoBERTa transformer models, and find that both models achieve high accuracy, precision, and specificity. The highest accuracy is achieved by RoBERTa, reaching an accuracy of 86.72%, a precision of 90.69% and a specificity of 90.53%. Furthermore, we explore the viability of using automated speech recognition for automatic transcription of audio samples from patient meetings. RoBERTa achieves an accuracy of 83.59% using transcripts generated by Google's automatic speech recognition, suggesting such methods may be viable for automating certain parts of the diagnostic process. In addition to the exploration of transformer models and their viability for dementia diagnostics, this paper provides a market analysis of a potential automated diagnostics tool utilizing transformer models. The analysis is based on a literature study and on two interviews; one with the CEO of a start-up providing automated dementia tests for healthcare professionals, and one with a psychologist researching dementia as well as potential methods of early diagnosis of dementia. With the interviews and literature study as a basis, we use the SWOT framework, and PEST analysis along with Porter's five forces framework to analyse the current market potential for such an automated tool. Despite detecting several obstacles and difficulties prior to market entry, we find significant potential for such a product given the current state of the market.