Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
Table 1: Comparison of GloVe, PCA, and word2vec for an embedding dimension of 500. 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 benchmark/dataset combination is shown in bold. The claims dataset contained only diagnosis codes and no drugs and so did not report results for the NDFRT benchmark.
['Data Source', 'Algorithm', 'Causative', 'Comorbidity', 'Semantic Type', 'NDFRT', 'Human Assessment']
We compared embeddings created by GloVe, word2vec, and PCA on our benchmarks to determine which algorithm and embedding dimension produced the best results across each individual dataset and on the combined data. These results are shown in Table 1. The best configuration was word2vec with an embedding dimension of 500, as it achieved the highest performance across nearly all benchmarks. Interestingly, we saw only a modest effect of embedding dimension on the benchmarks based on power (see Supplement). Also of note, the most direct comparison we could make to the original word2vec algorithm was using PubMed articles.