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.

本研究介绍了一种多功能退化建模的新框架，通过利用深度学习，预测健康退化分数并揭示老年人健康历史的潜在异质性，从而提供了对老年人退化多样效应和原因的高效估计和可解释洞察。一个真实案例研究证明了该方法的有效性，为准确建模老年衰退的复杂动态以及应对老龄化人口的医疗挑战做出了重要贡献。

社区老龄人群多功能退化建模的深度表示学习

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

Machine learning (ML) in healthcare presents numerous opportunities for
enhancing patient care, population health, and healthcare providers' workflows.
However, the real-world clinical and cost benefits remain limited due to
challenges in data privacy, heterogeneous data sources, and the inability to
fully leverage multiple data modalities. In this perspective paper, we
introduce "patchwork learning" (PL), a novel paradigm that addresses these
limitations by integrating information from disparate datasets composed of
different data modalities (e.g., clinical free-text, medical images, omics) and
distributed across separate and secure sites. PL allows the simultaneous
utilization of complementary data sources while preserving data privacy,
enabling the development of more holistic and generalizable ML models. We
present the concept of patchwork learning and its current implementations in
healthcare, exploring the potential opportunities and applicable data sources
for addressing various healthcare challenges. PL leverages bridging modalities
or overlapping feature spaces across sites to facilitate information sharing
and impute missing data, thereby addressing related prediction tasks. We
discuss the challenges associated with PL, many of which are shared by
federated and multimodal learning, and provide recommendations for future
research in this field. By offering a more comprehensive approach to healthcare
data integration, patchwork learning has the potential to revolutionize the
clinical applicability of ML models. This paradigm promises to strike a balance
between personalization and generalizability, ultimately enhancing patient
experiences, improving population health, and optimizing healthcare providers'
workflows.

介绍了一种名为 “拼合学习”(Patchwork learning，PL) 的新兴范式，通过整合来自不同数据模式的数据提供一个综合的方法来解决医疗保健中的数据隐私、异构数据来源以及无法完全利用多个数据模式的问题。PL 允许同时利用互补的数据源，同时保持数据隐私，从而使得 ML 模型更全面和具有可推广性。该范式具有医疗数据集成更全面的方法，有望革新 ML 模型的临床适用性。