  Objective: Currently, a major limitation for natural language processing
(NLP) analyses in clinical applications is that a concept can be referenced in
various forms across different texts. This paper introduces Multi-Ontology
Refined Embeddings (MORE), a novel hybrid framework for incorporating domain
knowledge 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 datasets
for the corpus-based component of MORE. For the ontology-based part, we use the
Medical Subject Headings (MeSH) ontology and three state-of-the-art
ontology-based similarity measures. In our approach, we propose a new learning
objective, modified from the Sigmoid cross-entropy objective function.
  Results and Discussion: We evaluate the quality of the generated word
embeddings using two established datasets of semantic similarities among
biomedical concept pairs. On the first dataset with 29 concept pairs, with the
similarity scores established by physicians and medical coders, MORE's
similarity 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 best
ontology-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 four
medical residents' similarity ratings, and that outperforms the skip-gram model
by 8.1% and the best ontology measure by 6.9%.
