Longitudinal Analysis of Disease Progression in the Elderly: An Approach to Mitigate the Burden of Frailty, Functional and Cognitive Decline

Mitigating age-related cognitive and functional decline is of paramount importance, especially in aging countries that are increasingly at risk of frailty and disability among the elderly population. This decline not only poses significant challenges for the elderly themselves but also contributes to an increased burden on caregivers. In particular, Alzheimer’s disease (AD) is the leading cause of cognitive decline in people aged 65 and older. It typically begins with mild memory problems that gradually worsen, leading to significant loss of brain function. Early detection of indicators of cognitive decline is critical to the diagnosis and treatment of neurodegenerative diseases, so acting as early as possible can improve the quality of life of older adults. This study analyzes the OASIS-3 dataset of Electronic Mental Health Records (EMHRs), focusing on identifying different trajectories of cognitive decline over time in stable and progressing individuals. Unlike many studies that analyze groups of patients at single points in time, this study uses a longitudinal approach to examine Alzheimer’s disease progression over time using clustering analysis. This study uses a k-means-based joint longitudinal data algorithm to cluster joint trajectories to identify distinct subgroups within a population according to their longitudinal profiles.