readme: ---
  license: gpl-3.0
  ---
  
  Pre-trained word embeddings using the text of published clinical case reports. These embeddings use 600 dimensions and were trained using the fasttext algorithm on published clinical case reports 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 = FastText.load('ft_oa_corp_600d.bin')
  
  # Return 100-dimensional vector representations of each word
  model.wv.word_vec('diabetes')
  model.wv.word_vec('cardiac_arrest')
  model.wv.word_vec('lymphangioleiomyomatosis')
  
  # Try out cosine similarity
  model.wv.similarity('copd', 'chronic_obstructive_pulmonary_disease')
  model.wv.similarity('myocardial_infarction', 'heart_attack')
  model.wv.similarity('lymphangioleiomyomatosis', 'lam')
  
  ```