Sharing Training Data for De-Identification

Update 2019-08-11: Our paper "Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records" was published at ACL 2019.

This is the code for my Master's thesis. It's about automatic transformations that can be applied to medical text data that…

• allow training a de-identification model (i.e. finding all protected information in text)
• do not allow attackers to infer any protected information.

Main Contribution

An adversarial deep learning architecture that learns a private representation of medical text. The representation model is an LSTM model that adds Gaussian noise of a trainable scale to its inputs and outputs.

The representation fulfills two invariance criteria that are both enforced by binary classifier LSTM adversary models that receive sequence pairs as inputs.

Left: Representations should be invariant to any protected information token being replaced with a neighbor in an embedding space (e.g. substituting a name or date).

Right: Looking up the same token sequence multiple times should result in a representation that is randomly different by a high enough degree that it could be the representation of a neighboring sequence.

   

Installation

• Checkout the repository including submodules. If you're doing a new clone:

  git clone --recurse-submodules git@github.com:maxfriedrich/deid-training-data.git

• Or, if you already cloned the repository:

  git submodule update --init

• Create a Conda environment for the project. If you want the environment name to be something other than deid-training-data or use tensorflow-gpu instead of tensorflow, adapt the environment.yml file before running this command. Then activate the environment.

  cd deid-training-data
  conda env create
  conda activate deid-training-data

• Download the English language model for spaCy:

  python -m spacy download en

• Verify that the environment is working by running the tests:

  DEID_TEST_CONFIG=1 nosetests --with-doctest

• Adapt the environment file.

• Decide with embeddings you want to use:

  • For FastText, get a fastText embeddings binary (4.5 GB download) as well as the corresponding .vec file of precomputed embeddings (590 MB download) and put it them the resources directory. Adapt the path here if necessary. Then convert the precomputed fastText embeddings to a {word: ind} dictionary and numpy matrix file:

    python -m deid.tools.embeddings --fasttext-precomputed

  • For GloVe, download a set of pre-trained word vectors and put it into the resources directory. Adapt the path and dimension here if you're not using the Wikipedia-pretrained 300d embeddings.

  • For ELMo, you don't need to download anything.

• Get the i2b2 data and extract training-PHI-Gold-Set1 into train_xml, training-PHI-Gold-Set2 into validation_xml, and testing-PHI-Gold-fixed into a test_xml directory.

• Fix one of the xml files where indices are offset after a special character:

  python -m deid.tools.fix_180-03 /path/to/validation_xml

• Convert the xml files with standoff annotations to an IOB2 format csv and a txt file containing the raw text:

  ./scripts/xml_to_csv

  The xml_to_csv script calls the deid.tools.i2b2_xml_to_csv module with the train_xml, validation_xml and test_xml directories. It will output some inconsistencies in the data (standoff annotation texts differ from original text), but we'll ignore those for now.

• Create an embeddings cache, again depending on your choice(s) of embeddings:

  • For FastText, this command writes all words from the train, test, and validation set to a pickle cache (5 minutes on my machine).

    python -m deid.tools.embeddings --fasttext-cache

  • For ELMo, this command looks up all sentences from the train, test, and validation set and writes them to many pickle files. This is slow, taking up to 3 hours.

    python -m deid.tools.embeddings --elmo-cache

Experiments

You can find these experiments in the deid/experiment directory:

• A basic experiment that can be used for training models on raw as well as augmented data
• An implementation of alternating adversarial training similar to Feutry et al. (2018)
• Evaluation experiments for automatic pseudonymization, discriminating real from automatically pseudonymized sentences, and the alternating training

To run an experiment:

• Modify the example config template and rename it to .yaml. Generate configs from it using the config tool:

  python -m deid.tools.config /path/to/config_template.yaml

  Specify the number of configs with the -n option. For a grid search instead of random samples, use the -a option (careful, this might generate thousands of configs depending on the hyperparameter space!).

• Run a single experiment from a config:

  python -m deid.experiment /path/to/config.yaml

  This will output predictions and save a history pickle to an experiment directory inside env.work_dir.

• Or set the DEID_CONFIG_DIR variable to the config directory and use the queue script to run all experiment configs from the ${DEID_CONFIG_DIR}/todo directory (they will be processed sequentially and moved to the ${DEID_CONFIG_DIR}/done directory).

  DEID_CONFIG_DIR=/path/to/config/dir ./scripts/queue

Evaluation

The evaluation using a modified version (2to3, minor fixes) of the official evaluation script is run automatically in the experiments. You can also call it like this to evaluate a directory of XML predictions:

python -m deid.tools.i2b2.evaluate phi /path/to/predictions /path/to/i2b2_data/validation_xml/