Authors: Suiyao Chen, Xinyi Liu, Yulei Li, Jing Wu, Handong Yao

Published on: April 08, 2024

Impact Score: 7.6

Arxiv code: Arxiv:2404.05613

Summary

• What is new: Introduction of a deep learning framework for multi-functional degradation modeling that accurately captures the multidimensional and heterogeneous nature of elderly disabilities.
• Why this is important: The increasing elderly population with multifunctional disabilities presents a challenge that traditional univariate regression-based methods, assuming population homogeneity, fail to address adequately.
• What the research proposes: A novel deep learning framework that predicts health degradation scores and uncovers latent heterogeneity in elderly health histories, offering efficient estimation and explainable insights.
• Results: The application of this framework in a real-case study showed significant effectiveness in accurately modeling the complex dynamics of elderly degradation.

Technical Details

Technological frameworks used: Deep learning-based framework for multi-functional degradation modeling

Models used: nan

Data used: Elderly health histories

Potential Impact

Healthcare providers, insurance companies, elderly care services, and healthcare technology firms

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