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

2024-09-20

arXiv.org

AI Summary

Early prediction of multimorbidity (MCC) is hindered by the absence of prior graph-structured patient relationships. Method: We propose a generative Graph Neural Network (GNN) framework that jointly learns both the patient relational graph structure and the predictive model—without requiring predefined topology. Our approach integrates a Graph Variational Autoencoder (GVAE), Laplacian-regularized GNNs, and context-aware Bandit optimization, coupled with a dynamic graph refinement strategy leveraging Laplacian regularization. Contribution/Results: Evaluated on a cohort of 1,592 patients, our framework achieves significantly higher prediction accuracy than ε-greedy and standard multi-armed Bandit baselines. It demonstrates the efficacy and novelty of data-driven, end-to-end graph learning for clinical risk prediction—enabling adaptive, interpretable, and topology-agnostic modeling of complex comorbidity patterns.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

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.

Problem

• Predicting multiple chronic conditions (MCC) for early intervention.
• Overcoming GNN reliance on predefined graph structures for MCC.
• Enhancing MCC prediction accuracy using generative GNN frameworks.

Innovation

• Graph Variational Autoencoder for patient data relationships
• Bandit-optimized Graph Neural Network for graph selection
• Laplacian regularization to refine graph structure