KEM-IoMT: Knowledge graph embedding-enhanced accurate medical service recommendation against diabetes

The Internet of Medical Things (IoMT)-enhanced Recommender System (RS) acquired swift advancement in configuring diverse medical data into intelligent systems to generate personalized medical services. However, due to the heterogeneous and complex nature of the diabetes data, generating accurate and context-sensitive service recommendations remains challenging. Additionally, existing RSs do not extend their knowledge-bases by incorporating user-reviews and current updates on the given disease alongside the medical data. Thus, this paper introduces Knowledge graph Embedding-enhanced accurate Medical service recommendation (KEM) in the IoMT, aiming to enhance the precision of RS for diabetes care. The KEM mainly collects user reviews and online data about the disease, preprocesses the collected data, and transforms it into the Knowledge Graph (KG). The model embeds the KG and encapsulates the embedding representations into the independent latent factors through the Graph Neural Network. Moreover, the KEM employs Deep Matrix Factorization to compute the latent factors and obtain the required relations for recommendation. Extensive experiments on real-world data demonstrate the effectiveness of the KEM model in enhancing performance compared to baseline methods.