Authors: jrzech, eko
Provides a library of methods for automatically inferring labels for a corpus of radiological reports given a set of manually-labeled data. These methods are described in our publication Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports.
To configure your own local instance (assumes Anaconda is installed):
git clone https://www.github.com/aisinai/rad-report-annotator.git
cd rad-report-annotator
conda env create -f environment.yml
source activate rad_env
python -m ipykernel install --user --name rad_env --display-name "Python (rad_env)"
Note as of Oct 11, 2022: this conda environment builds on Linux and Windows, but not on Mac as older versions of gensim for Mac are not available in conda-forge.
To see a demo of the library on data from the Indiana University Chest X-ray Dataset (Demner-Fushman et al.), please open Demo Notebook.ipynb and run all cells.