Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

I. Introduction

Being a chronic and degenerative condition, dementia has a high prevalence within the elderly affecting 47.5 million people globally [1]. The most common cause of dementia being Alzheimer's Disease (AD) making up 60–80% of cases [2]. Within the U.K., dementia affects 850 000 people with a forecast rate of prevalence of 1 million by 2025 and 2 million by 2051 [3]. The resulting cost of dementia in the U.K. totals £26.3 billion with two thirds being paid for by dementia patients and families in private social care as of 2018 [3].