From UMLS to ClinicalBERT: Deep hybrid-health recommender system (Dh_HRS)

BackgroundThe digitalisation of healthcare and the development of clinical decision support systems represent a major step towards improving care quality and reducing medical errors.ObjectiveTo develop a hybrid medical recommendation system that delivers robust, interpretable predictions for diagnoses, prescriptions, and clinical notes to enhance clinical decision support.MethodsWe propose a hybrid framework that uses Unified Medical Language System (UMLS) ontology and Concept Unique Identifiers (CUIs) to standardise patient profiles. ClinicalBERT was employed to generate contextual embeddings capturing linguistic and semantic nuances. These embeddings were integrated into a knowledge graph and processed using a Graph Convolutional Network (GCN) to enable contextual aggregation of semantic neighbourhoods. The system was evaluated experimentally against recent benchmark models using the MIMIC-III, PubMed, and ICD-10-CM datasets.ResultsExperiments conducted on the benchmark datasets demonstrated that the proposed hybrid approach achieved superior performance compared with recent existing benchmark models.ConclusionCombining UMLS ontology, ClinicalBERT contextual embeddings, and GCN-driven knowledge graphs enables robust and interpretable clinical predictions, representing a significant advancement in clinical decision support systems.