Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population
This addresses healthcare challenges for the aging population by providing a more accurate model of elderly degradation, though it is incremental as it builds on deep learning methods for a specific domain.
The study tackled the problem of modeling multifunctional disabilities in the aging population by introducing a deep learning framework that captures multidimensional and heterogeneous health degradation, predicting scores and uncovering latent heterogeneity from health histories.
As the aging population grows, particularly for the baby boomer generation, the United States is witnessing a significant increase in the elderly population experiencing multifunctional disabilities. These disabilities, stemming from a variety of chronic diseases, injuries, and impairments, present a complex challenge due to their multidimensional nature, encompassing both physical and cognitive aspects. Traditional methods often use univariate regression-based methods to model and predict single degradation conditions and assume population homogeneity, which is inadequate to address the complexity and diversity of aging-related degradation. This study introduces a novel framework for multi-functional degradation modeling that captures the multidimensional (e.g., physical and cognitive) and heterogeneous nature of elderly disabilities. Utilizing deep learning, our approach predicts health degradation scores and uncovers latent heterogeneity from elderly health histories, offering both efficient estimation and explainable insights into the diverse effects and causes of aging-related degradation. A real-case study demonstrates the effectiveness and marks a pivotal contribution to accurately modeling the intricate dynamics of elderly degradation, and addresses the healthcare challenges in the aging population.