Graph Neural Network and Sentence Transformer Embeddings for SNOMED CT concepts

Embeddings for SNOMED CT concepts produced by Graph Neural Networks (GNNs) or Sentence Transformer model. Each NPZ file encodes a dictionary, which links the ID of a SNOMED CT concept to its corresponding embedding. Files base_mini_lm_dict.npz and fine_tuned_mini_lm_dict.npz contain the embeddings of the sentence transformer models, where the former is using the base MiniLM model and the latter is using the fine-tuned MiniLM model on the concept similarity task. Files gnn_mul_sct_dict.npz and gnn_sim_sct_dict.npz contain the embeddings produced by a GNN on a dataset produced by transforming the SNOMED CT ontology and on the task of concept similarity. These embeddings were generated and studied in the paper Assessing the Effectiveness of Embedding Methods in Capturing Clinical Information from SNOMED CT () and more information can also be found in the following repository: https://github.com/JavierCastellD/AssessingSNOMEDEmbeddings.