FasTag

Description

FasTag is a text classification document for the rapid triaging of clinical documents.

Citation

For citation and more information refer to:

FasTag application:

Venkataraman, Guhan Ram, et al. "FasTag: Automatic text classification of unstructured medical narratives." PLoS one 15.6 (2020): e0234647.

FasTag initial implementation:

S. Ayyar, O. J. Bear Don't Walk IV (2017). "Tagging Patient Notes with ICD-9 Codes". Technical Report. Stanford University.

To Train on Default Dataset (MIMIC)

Setup Server with TensorFlow-GPU

Make sure that you have a GPU with at least 50GB RAM for training this neural network. Otherwise, the computations become intractable.

We have used Azure NV6 and Amazon P2 (with Deep Learning AMI) in the past. The benefit of the Deep Learning AMI is that it comes with a version of TensorFlow-GPU installed with all of the necessary NVIDIA/CUDA GPU driver prerequisites also functioning.

Run Preprocessing

Clone the repository to both your local machine and the compute instance. The script createAdmissionNoteTable.R from src/textPreprocessing is meant to process the MIMIC database and extract only the relevant information (notes and corresponding ICD-9 codes).
Gain access to the MIMIC-III database, and then download the files labeled DIAGNOSES_ICD.csv and NOTEEVENTS.csv here.
Replace the absolute paths to these two .csvs in lines 25 and 30 in the R script, and run the script. You should end up with three files: icd9NotesDataTable_train.csv, icd9NotesDataTable_valid.csv, and icd9NotesDataTable.csv. These will be your training and validation sets.
scp these three files to the server by running these commands:

scp path/to/icd9NotesDataTable_train.csv username@domain:~/clinicalNoteTagger/data/

scp path/to/icd9NotesDataTable_valid.csv username@domain:~/clinicalNoteTagger/data/

scp path/to/icd9NotesDataTable.csv username@domain:~/clinicalNoteTagger/data/

In a new terminal window, ssh username@domain and cd into clinicalNoteTagger/data. You should see the newly scped .csv files here. Then, run unzip newgloveicd9.txt.zip. This generates the length-300 word vectors that the words in the notes will be converted to.
Now it's time to actually train the model.

Building the model

Run jupyter notebook --no-browser --port=8888; lsof -ti:8888 | xargs kill -9 on the server. This will boot up the instance on port 8888 on the server and instructs the server to clear the port for reuse after ending the notebook session.
Run ssh -N -f -L localhost:8889:localhost:8888 ssh-login@ssh_ip on your local machine (use -i path/to/.pem/file if necessary)
In a browser tab, enter address https://localhost:8889. This should open up a jupyter notebook instance with all the files in the directory.
When finishing manipulation in the jupyter notebook and stopping listening on localhost:8889, run lsof -ti:8889 | xargs kill -9 on your local machine to clear port 8889.
Finally, execute the cells in the notebook mimic.ipynb in the builds folder which will read in the data and train the model.

Evaluating the model

At the end of the notebook, model weights are also saved for reusing later. Use prediction_evaluation_mimic.ipynb in the prediction_evaluations folder to evaluate trained predictions by the Clinical Note Tagger.

There is also a notebook that shows how we trained relevant baseline classifiers (Decision Trees [DTs] and Random Forests [RFs]) and performed a hyperparameter search to determine the best performance.

Other Datasets

FasTag can handle other datasets besides that of MIMIC, as long as your dataset of choice has clinical notes in one column and labels (hyphen-separated if multiple for one row, e.g. "1-2" or "cat:1-cat:2") in another. The numbers associated with these columns are inputted in the third coding cell in builds notebooks, e.g. mimic.ipynb. You can observe the similarities differences between mimic.ipynb and csu.ipynb, for example, in order to see how general the procedure is for building models, and can plug-and-play your dataset as needed.

prediction_evaluations notebooks can be run for datasets that are validated on their respective data, and crosschecks notebooks can be run for evaluating models that you have built using builds notebooks but wish to test on other datasets (but, you must ensure that the labels are consistent).

Development status

The FasTag clinical note tagger was created in the Rivas Lab at Stanford University by Oliver Bear Don't Walk IV, Sandeep Ayyar, and Manuel Rivas for a class project in CS224N in the Winter of 2017. It was extended and continues to develop under Guhan Venkataraman in the same lab.

Active developers

Guhan Venkataraman (guhan[at]stanford[dot]edu)

wvandermerwe/FasTag