Integrating Meta-Learning Methods with Spatiotemporal Graph Neural Networks for Critical Heart Disease Outcome Prediction from Electronic Health Records
Received: 8 April 2025 | Revised: 16 May 2025, 22 June 2025, and 14 July 2025 | Accepted: 19 July 2025 | Online: 6 October 2025
Corresponding author: Muzamil Basha Syed
Abstract
Extracting valuable insights from Electronic Health Record (EHR) data is challenging since they are high-dimensional, sparse, and with evolving temporal patterns. In this paper, an advanced framework that combines Meta-Learning with Spatiotemporal Graph Neural Networks (ST-GNNs) was developed to enhance disease prediction models utilizing EHR data and their robustness against data variability. The novelty of this approach is the utilization of meta-learning on one EHR dataset with one healthcare task for the acquisition of a single model that can generalize to other EHR datasets and healthcare tasks. Finally, to understand the spatiotemporal relationship of patients, the proposed framework leverages ST-GNNs for capturing complex spatiotemporal relationships in patient data and obtaining a comprehensive understanding of disease progression over time. The main goal of this study is to improve disease prediction using model parameters that adapt dynamically using meta-learning and effectively learning temporal dependencies on EHR data using GNNs. The model is trained with an adaptive learning mechanism where it is constantly fine-tuning itself according to the evolving data patterns. The proposed approach attains superior performance in terms of accuracy and training time saved in comparison with state-of-the-art deep learning models. Moreover, the integration of spatiotemporal GNNs significantly improves the interpretability of predicted risk trajectories, consequently supporting better decision-making in personalized healthcare.
Keywords:
electronic health records, meta-learning, spatiotemporal graph neural networks, disease prediction, temporal data modeling, adaptive learning, healthcare informaticsDownloads
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