 Pre-trained word embeddings using the text of published biomedical manuscripts. These embeddings use 300 dimensions and were trained using the word2vec algorithm on all available manuscripts found in the [PMC Open Access Subset](https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/). See the paper here: https://pubmed.ncbi.nlm.nih.gov/34920127/  Citation:  ``` @article{flamholz2022word,   title={Word embeddings trained on published case reports are lightweight, effective for clinical tasks, and free of protected health information},   author={Flamholz, Zachary N and Crane-Droesch, Andrew and Ungar, Lyle H and Weissman, Gary E},   journal={Journal of Biomedical Informatics},   volume={125},   pages={103971},   year={2022},   publisher={Elsevier} } ```  