Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Table 2: Comparison of the performance of cui2vec to previously published embeddings. Columns 1-4 report power to detect known relationships and column 5 reports the Spearman correlation between human assessments of concept similarity and cosine similarity from the embeddings. The best result for each each comparison is shown in bold.
['Source', 'Causative', 'Comorbidity', 'NDFRT', 'Semantic Type', 'Human Assessment']
The first comparison was against 300-dimensional embeddings for 15,905 concepts (of which 12,568 were in common with cui2vec) derived from a claims database of 4 million patients. We observed that cui2vec outperformed the reference embeddings in most tasks, in some instances by a substantial margin, though the embeddings from Choi et al. had the edge in the human assessment benchmark. Next, we compared 300-dimension embeddings for 28,394 concepts derived from the same set of clinical notes in Finlayson et al.[Finlayson2014] published as part of Choi et al.[Choi2016] In total, there were 21,789 concepts in common between cui2vec and this set of embeddings. Here cui2vec was again better in most benchmarks, in some cases by a large margin. Finally, we compared cui2vec against 200-dimensional embeddings for 59,266 concepts derived from 348,566 PubMed abstracts, first published in De Vine et al.[de2014medical] There were 33,376 concepts in common that were used for benchmarking.