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Objective: Currently, a major limitation for natural language processing(NLP) analyses in clinical applications is that a concept can be referenced invarious forms across different texts. This paper introduces Multi-OntologyRefined Embeddings (MORE), a novel hybrid framework for incorporating domainknowledge from multiple ontologies into a distributional semantic model,learned from a corpus of clinical text.  Materials and Methods: We use the RadCore and MIMIC-III free-text datasetsfor the corpus-based component of MORE. For the ontology-based part, we use theMedical Subject Headings (MeSH) ontology and three state-of-the-artontology-based similarity measures. In our approach, we propose a new learningobjective, modified from the Sigmoid cross-entropy objective function.  Results and Discussion: We evaluate the quality of the generated wordembeddings using two established datasets of semantic similarities amongbiomedical concept pairs. On the first dataset with 29 concept pairs, with thesimilarity scores established by physicians and medical coders, MORE'ssimilarity scores have the highest combined correlation (0.633), which is 5.0%higher than that of the baseline model and 12.4% higher than that of the bestontology-based similarity measure.On the second dataset with 449 concept pairs,MORE's similarity scores have a correlation of 0.481, with the average of fourmedical residents' similarity ratings, and that outperforms the skip-gram modelby 8.1% and the best ontology measure by 6.9%.

Multi-Ontology Refined Embeddings (MORE): A Hybrid Multi-Ontology and Corpus-based Semantic Representation for Biomedical Concepts