Enhancing Service Recommendation in Smart Healthcare Networks via Pattern‐Driven Probabilistic Representation Learning

Recently, virtual hospitals have revolutionized healthcare by leveraging technologies like the Internet of Things (IoT), Big Data, Cloud Computing and Service‐Oriented Computing. These paradigms abstract and deliver diverse medical resources (e.g., treatment plans, doctors, smart medical devices) as services. However, the exponential growth of healthcare services, coupled with the dynamic and multi‐relational nature of healthcare networks, presents challenges in selecting optimal services tailored to patients' critical conditions. Current systems inadequately model healthcare contexts and provide limited support for the evolving state of patients or conflicts in treatment plans. This paper addresses these issues by introducing a recommendation method that combines knowledge graph technology and graph neural networks for probabilistic healthcare service recommendation. The proposed approach incorporates three key contributions: (1) a semantic and multi‐relational modelling of virtual hospital data using an uncertainty‐aware healthcare knowledge graph; (2) probabilistic embeddings for healthcare entities to manage the inherent uncertainty of healthcare data; and (3) a pattern‐driven recommendation mechanism that identifies optimal treatment plans by matching requirement and service patterns, considering patient constraints and uncertain health information. Experimental studies confirmed our approach's ability to handle uncertainty and ensure semantic coherence, thereby enhancing the accuracy and adaptability of healthcare recommendations in smart healthcare networks.