Deep Representation Learning for Multi-functional Degradation Modeling of Community-dwelling Aging Population

TL;DR

This paper presents a deep learning framework for modeling complex, multidimensional health degradation in the elderly, capturing heterogeneity and providing explainable insights to improve healthcare predictions.

Contribution

It introduces a novel deep learning-based approach for multi-functional degradation modeling that accounts for heterogeneity and multidimensional aspects of aging-related disabilities.

Findings

Effective prediction of health degradation scores

Uncovering latent heterogeneity in elderly health data

Improved modeling of complex aging processes

Abstract

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.