Chronic diseases affect millions of people worldwide, making it a significant issue for the efficient and secure management of patient data. Most traditional chronic disease management systems fail to manage data in real time and struggle with interoperability and security issues. Scientists have integrated wearable devices with sensors and AI to advance traditional approaches towards CDM. These technologies enhance the monitoring and prediction of CD but still face problems related to data security. The scenario has forced researchers to investigate blockchain technology in protecting patient data. Traditional blockchain systems face the challenge of scalability, privacy, and flexible permission control. In order to address the above issues, we propose the integration of Hyperledger Fabric into CDM systems. Hyperledger Fabric is a permissioned blockchain framework that excels in data security, scalability, and customizable access control. While this study focuses on conceptualizing a blockchain-based framework, the AI-driven components have been implemented and evaluated using real-world datasets, achieving superior results. Evaluation results demonstrate an average precision of 91.79%, sensitivity of 94.72%, accuracy of 96.00%, and F1-score of 0.92, outperforming state-of-the-art approaches. The integration of Hyperledger Fabric is suggested as a future improvement in the system to ensure tamper-proof storage and safe sharing of patient data. This addresses the critical challenges in CDM systems, thereby making this study a pioneering one that presents the transformational potential of blockchain technology with AI for chronic disease management.

Network alignment, the task of identifying the same entities across multiple networks, has recently gained immense popularity in industry and academia. However, most existing alignment methods consider networks static, merely a snapshot of the time-evolving real networks where the nodes, links, and attributes are bound to change over time. The existing methods cannot capture these intrinsic dynamic network changes and cannot be applied directly to the evolving real-network scenario. Even extending these static methods to update their model parameters by retraining dynamically suffers from high computational complexity or simple sequential training suffers from compromised predictions due to distribution drifts in the networks. Moreover, dealing with a pair of evolving networks poses extra challenges of their domain differences and different evolution rates and patterns. Hence to overcome these challenges, we propose EvoAlign, an end-to-end information replay-based continual learning framework built upon Graph Neural Networks (GNNs) for the alignment of evolving networks. EvoAlign employs the concept of uncertainty in model predictions to identify and address two key aspects: detecting new patterns and preserving historical patterns. It also uses a novel shift-induced regularizer to handle distribution drift and domain differences in evolving networks. We empirically show that our method outperforms the existing state-of-the-art alignment methods on three real datasets.

Network alignment techniques that map the same entities across multiple networks assume that the mapping nodes in two different networks have similar attributes and neighborhood proximity. However, real-world networks often violate such assumptions, having diverse attributes and structural properties. Node mapping across such structurally heterogeneous networks remains a challenge. Although capturing the nodes’ entire neighborhood (in low-dimensional embeddings) may help deal with these characteristic differences, the issue of over-smoothing in the representations that come from higherorder learning still remains a major problem. To address the above concerns, we propose SAlign: a supervised graph neural attention framework for aligning structurally heterogeneous networks that learns the correlation of structural properties of mapping nodes using a set of labeled (mapped) anchor nodes. SAlign incorporates nodes’ graphlet information with a novel structure-aware cross-network attention mechanism that transfers the required higher-order structure information across networks. The information exchanged across networks helps in enhancing the expressivity of the graph neural network, thereby handling any potential over-smoothing problem. Extensive experiments on three real datasets demonstrate that SAlign consistently outperforms the state-of-the-art network alignment methods by at least 1.3-8% in terms of accuracy score. The code is available at https://github.com/shruti400/SAlign for reproducibility.