A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network

📄 arXiv: 2409.13671v3 📥 PDF

作者: Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P Fisher-Hoch, Joseph B Mccormick

分类: cs.LG

发布日期: 2024-09-20 (更新: 2025-06-10)

备注: This work has been accepted for publication in the IEEE Journal of Biomedical and Health Informatics

期刊: IEEE J. Biomed. Health Inform., 2025

DOI: 10.1109/JBHI.2025.3578532

💡 一句话要点

提出生成框架以解决多重慢性病预测问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)

关键词: 图神经网络 生成模型 慢性病预测 个性化医疗 上下文赌博机 拉普拉斯正则化 患者数据分析

📋 核心要点

  1. 现有的图神经网络在多重慢性病预测中面临依赖现有图结构的挑战，限制了其应用。
  2. 本文提出了一种生成框架，利用图变分自编码器构建底层图结构，从而增强MCC的预测能力。
  3. 在大规模患者队列上验证了该方法的有效性，显示出相较于传统算法的显著提升。

📝 摘要（中文）

预测多重慢性病（MCC）的出现对于早期干预和个性化医疗至关重要，因为MCC显著影响患者的健康结果和医疗成本。图神经网络（GNN）在建模复杂图数据方面表现出色，但其依赖于现有图结构的特性在MCC中面临挑战。为此，本文提出了一种新颖的生成框架，通过利用数据分布构建代表性的底层图结构，以增强MCC的预测分析。该框架采用图变分自编码器（GVAE）捕捉患者数据中的复杂关系，并生成多样的患者随机相似图，随后通过GNN和新颖的拉普拉斯正则化技术进行处理，提升MCC的预测准确性。通过与$ ext{ε}$-贪婪和多臂赌博机算法的对比验证了该方法的有效性。

🔬 方法详解

问题定义：本文旨在解决多重慢性病（MCC）预测中的图结构缺失问题。现有方法依赖于预先定义的图结构，难以适应动态变化的患者数据。

核心思路：提出的生成框架通过图变分自编码器（GVAE）构建底层图结构，捕捉患者数据中的复杂关系，从而生成多样的患者随机相似图。

技术框架：整体架构包括三个主要模块：首先，使用GVAE从患者数据中学习潜在的图结构；其次，生成的随机相似图通过图神经网络（GNN）进行处理；最后，采用上下文赌博机算法优化图结构，迭代选择最佳图。

关键创新：该框架的创新在于结合了生成模型与图神经网络，克服了传统GNN对固定图结构的依赖，能够动态适应患者数据的变化。

关键设计：在设计中，采用拉普拉斯正则化技术来逐步优化图结构，同时上下文赌博机算法用于评估和选择最佳图，确保模型的收敛性和预测准确性。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，所提出的上下文赌博机算法在大规模患者队列（n = 1,592）上表现优于传统的$ ext{ε}$-贪婪和多臂赌博机算法，显著提高了MCC预测的准确性，验证了该方法的有效性和实用性。

🎯 应用场景

该研究具有广泛的应用潜力，特别是在个性化医疗和公共卫生领域。通过准确预测多重慢性病的风险，医疗机构可以实施更有效的早期干预措施，从而改善患者的健康结果并降低医疗成本。未来，该框架还可扩展至其他疾病预测和健康管理领域。

📄 摘要（原文）

Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.