Clinical Document Classification Pipeline

• Written by Wei-Hung Weng (HMS, MGH)
• Created: Nov 9, 2016
• Latest update: Nov 27, 2017
• Please contact the author with errors found.
• ckbjimmy {AT} mit {DOT} edu

Quick Start

1. git clone the repository
2. python setup.py install
3. Go to the directory
4. Run sh test_model.sh

Word embedding-CRNN

1. Download fasttext embedding from fasttext website (Use either wiki-news-300d-1M.vec or wiki-news-300d-1M-subword.vec, depends on your text)
2. use python EmbCRNN.py [text_path] [label_path] [embedding_path]

Paragraph vector

1. use python Doc2vec.py [text_path] [label_path]

If you use this code, please kindly cite the paper for this GitHub project (see below for BibTex):

@article{weng2017medical,
    title   = {Medical Subdomain Classification of Clinical Notes Using a Machine Learning-Based Natural Language Processing Approach.},
    author  = {Weng, Wei-Hung and Wagholikar, Kavishwar B. and McCray, Alexa T. and Szolovits, Peter and Chueh, Henry C.},
    journal = {BMC Medical Informatics and Decision Making.},
    year    = {2017},
    number  = {17},
    page    = {155},
    note    = {\mbox{doi}:\url{10.1186/s12911-017-0556-8}}
}

The code belongs to Wei-Hung Weng and Laboratory of Computer Science, Massachusetts General Hospital.