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Adversarial Learning of Lexcialized Knowledge Embeddings for the Unified Medical Language System

License: GNU General Public License v3.0

Python 9.86% Shell 1.13% Jupyter Notebook 89.01%

umls-embeddings's Introduction

The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts

Code for the paper The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts published in Journal of the American Medical Informatics Association. Please cite:

@article{10.1093/jamia/ocaa205,
    author = {Weinzierl, Maxwell A and Maldonado, Ramon and Harabagiu, Sanda M},
    title = "{The impact of learning Unified Medical Language System knowledge embeddings in relation extraction from biomedical texts}",
    journal = {Journal of the American Medical Informatics Association},
    year = {2020},
    month = {10},
    issn = {1527-974X},
    doi = {10.1093/jamia/ocaa205},
    url = {https://doi.org/10.1093/jamia/ocaa205},
    note = {ocaa205},
}

Requires Tensorflow version 1.9. See requirements.txt for details

Data Preprocessing:

First download/extract the UMLS. This project assumes the UMLS files are laid out as such: <UMLS_DIR>/ META/ MRCONSO.RRF MRSTY.RRF NET/ SRSTRE1 More details coming soon!