RESUMO
Clinical term embeddings are traditionally obtained using corpus-based methods, however, these methods cannot incorporate knowledge about clinical terms which is already present in medical ontologies. On the other hand, graph-based methods can obtain embeddings of clinical concepts from ontologies, but they cannot obtain embeddings for clinical terms and words. In this paper, a novel method is presented to obtain embeddings for clinical terms and words from the SNOMED CT ontology. The method first obtains embeddings of clinical concepts from SNOMED CT using a graph-based method. Next, these concept embeddings are used as targets to train a deep learning model to map clinical terms to concepts embeddings. The learned model then provides embeddings for clinical terms and words as well as maps novel clinical terms to their embeddings. The embeddings obtained using the method out-performed corpus-based embeddings on the task of predicting clinical term similarity on five benchmark datasets. On the clinical term normalization task, using these embeddings simply as a means of computing similarity between clinical terms obtained accuracy which was competitive to methods trained specifically for this task. Both corpus-based and ontology-based embeddings have a limitation that they tend to learn similar embeddings for opposite or analogous terms. To counter this, we also introduce a method to automatically learn patterns that indicate when two clinical terms represent the same concept and when they represent different concepts. Supplementing the normalization process with these patterns showed improvement. Although clinical term embeddings obtained from SNOMED CT incorporate ontological knowledge which is missed by corpus-based embeddings, they do not incorporate linguistic knowledge which is needed for sentence-based tasks. Hence combining ontology-based embeddings with corpus-based embeddings is an avenue for future work.