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.

翻译：预测多种慢性病（MCC）的发生对于早期干预和个性化医疗至关重要，因为MCC显著影响患者预后和医疗成本。图神经网络（GNNs）是建模复杂图数据（如MCC数据）的有效方法。然而，GNNs面临的一个重大挑战是其对现有图结构的依赖，而MCC数据中往往缺乏现成的图结构。为解决这一挑战，我们提出一种新颖的GNN生成式框架，该框架利用数据分布构建具有代表性的底层图结构，以增强MCC的预测分析能力。我们的框架采用图变分自编码器（GVAE）来捕捉患者数据中的复杂关系。这有助于全面理解个体健康轨迹，并在保留原始特征集的同时，促进生成多样化的患者随机相似性图。通过GVAE解码器生成的这些患者随机相似性图变体，随后由GNN结合新颖的拉普拉斯正则化技术进行处理，以随时间优化图结构并提升MCC的预测精度。我们设计了一个上下文Bandit模型，用于评估随机生成的图，并迭代识别出最适合GNN模型的性能最优图，直至模型收敛。我们在一个大型MCC患者队列（n = 1,592）中，将所提出的上下文Bandit算法与ε-Greedy及多臂Bandit算法进行了性能验证。这些进展凸显了所提方法在变革预测性医疗分析方面的潜力，为MCC管理实现更个性化和前瞻性的方法提供了可能。