readme: ---
  license: gpl-3.0
  ---
  
  Pre-trained word embeddings using the text of published biomedical manuscripts. These embeddings use 600 dimensions and were trained using the GloVe algorithm on all published 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}
  }
  ```
  
  ## Quick start
  
  Word embeddings are compatible with the [`gensim` Python package](https://radimrehurek.com/gensim/) format.
  
  First download the files from this archive. Then load the embeddings into Python.
  
  
  ```python
  
  from gensim.models import FastText, Word2Vec, KeyedVectors # KeyedVectors are used to load the GloVe models
  
  # Load the model
  model = KeyedVectors.load_word2vec_format('gl_600_oa_all.txt')
  
  # Return 100-dimensional vector representations of each word
  model.word_vec('diabetes')
  model.word_vec('cardiac_arrest')
  model.word_vec('lymphangioleiomyomatosis')
  
  # Try out cosine similarity
  model.similarity('copd', 'chronic_obstructive_pulmonary_disease')
  model.similarity('myocardial_infarction', 'heart_attack')
  model.similarity('lymphangioleiomyomatosis', 'lam')
  
  ```