  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.
