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An overview of biomedical concept embeddings trained on large-scale free-text corpora.

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NIAID Data Ecosystem2026-03-11 收录
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https://figshare.com/articles/dataset/An_overview_of_biomedical_concept_embeddings_trained_on_large-scale_free-text_corpora_/12184371
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Repository: the scope of concepts. Corpora: the training collection. Note that for EHR (electronic health records) and Claims (medical claims), the size is the number of patients, whereas for Wikipedia, PubMed (abstracts), and PMC (full-text articles), the size is the number of documents. #Concepts: the number of distinct concepts in the embedding. Method: the method for training embeddings. PCA: principle component analysis. PMI: pointwise mutual information. Intrinsic evaluation: a focus on applications that directly use the similarity between the vectors produced by word embeddings, such as word-pair similarity and relatedness. Extrinsic evaluation: a focus on downstream applications that use only word embeddings as an intermediate component. For example, the last study evaluated the effectiveness of concept embeddings for heart-failure prediction. Availability: whether the studies made the embeddings publicly available (we accessed on 04/20/2019).
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2020-04-23
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