残疾老年人的长期护理计划推荐:二部图转换器和自我监督方法。

IF 4.6 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Chunlong Miao, Jingjing Luo, Yan Liang, Hong Liang, Yuhui Cen, Shijie Guo, Hongliu Yu
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引用次数: 0

摘要

背景:随着全球人口老龄化和医疗系统的进步,医疗机构和家庭环境中的长期护理对残疾老年人至关重要。然而,这些个体的多样化和分散的护理需求使得制定有效的长期护理计划严重依赖于专业护理人员,即使是经验丰富的护理人员也可能在护理计划制定过程中犯错误或面临混乱。因此,对于能够为临床状况稳定的残疾老年人推荐全面长期护理计划的智能系统,存在着严格的需求。目的:本研究旨在利用深度学习方法,为残障老年人提供综合护理方案。方法:采用二部图对老年人残疾护理数据进行建模。此外,我们采用基于预测的图自监督学习(SSL)方法来挖掘图节点的深度表示。此外,我们提出了一种新的图转换器架构,该架构结合特征向量中心性来增强节点特征,并使用图结构信息作为自关注机制的参考。最后,我们提出了二部图转换(BiT)模型来提供个性化的长期护理计划建议。结果:我们构建了一个由1917个节点和195240个边组成的二部图。所提出的模型表现出色,在护理计划建议方面的F1总分为0.905。每个护理服务项目的F1平均得分为0.897,表明BiT模型能够准确地选择服务,并有效地平衡了不正确选择和错过选择之间的权衡。讨论:本文提出的BiT模型通过利用二部图建模和图SSL,展示了改善长期护理计划建议的强大潜力。这种方法通过提供个性化和数据驱动的建议,解决了人工护理计划的挑战,如效率低下、偏见和错误。虽然该模型在普通护理项目上表现出色,但它在罕见或复杂服务上的性能可以通过进一步改进得到增强。这些发现突出了该模型提供可扩展的、人工智能驱动的解决方案来优化护理计划的能力,尽管未来的研究应探索其在不同医疗环境和服务类型中的适用性。结论:与以往的研究相比,本文提出的新模型有效地学习了二部图的潜在拓扑,并取得了更好的推荐性能。我们的研究证明了SSL和图形转换器在为残疾老年人推荐长期护理计划中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.

Background: With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions.

Objective: This study aims to utilize deep learning methods to recommend comprehensive care plans for the older adults with disabilities.

Methods: We model the care data of older adults with disabilities using a bipartite graph. Additionally, we employ a prediction-based graph self-supervised learning (SSL) method to mine deep representations of graph nodes. Furthermore, we propose a novel graph Transformer architecture that incorporates eigenvector centrality to augment node features and uses graph structural information as references for the self-attention mechanism. Ultimately, we present the Bipartite Graph Transformer (BiT) model to provide personalized long-term care plan recommendation.

Results: We constructed a bipartite graph comprising of 1917 nodes and 195 240 edges derived from real-world care data. The proposed model demonstrates outstanding performance, achieving an overall F1 score of 0.905 for care plan recommendations. Each care service item reached an average F1 score of 0.897, indicating that the BiT model is capable of accurately selecting services and effectively balancing the trade-off between incorrect and missed selections.

Discussion: The BiT model proposed in this paper demonstrates strong potential for improving long-term care plan recommendations by leveraging bipartite graph modeling and graph SSL. This approach addresses the challenges of manual care planning, such as inefficiency, bias, and errors, by offering personalized and data-driven recommendations. While the model excels in common care items, its performance on rare or complex services could be enhanced with further refinement. These findings highlight the model's ability to provide scalable, AI-driven solutions to optimize care planning, though future research should explore its applicability across diverse healthcare settings and service types.

Conclusions: Compared to previous research, the novel model proposed in this article effectively learns latent topology in bipartite graphs and achieves superior recommendation performance. Our study demonstrates the applicability of SSL and graph transformers in recommending long-term care plans for older adults with disabilities.

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来源期刊
Journal of the American Medical Informatics Association
Journal of the American Medical Informatics Association 医学-计算机:跨学科应用
CiteScore
14.50
自引率
7.80%
发文量
230
审稿时长
3-8 weeks
期刊介绍: JAMIA is AMIA''s premier peer-reviewed journal for biomedical and health informatics. Covering the full spectrum of activities in the field, JAMIA includes informatics articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy. JAMIA''s articles describe innovative informatics research and systems that help to advance biomedical science and to promote health. Case reports, perspectives and reviews also help readers stay connected with the most important informatics developments in implementation, policy and education.
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