Adversarial Learning of Privacy-Preserving Text Representations
for De-Identification of Medical Records
Abstract
De-identification is the task of detecting protected health information (PHI) in medical
text. It is a critical step in sanitizing electronic
health records (EHRs) to be shared for research. Automatic de-identification classifiers
can significantly speed up the sanitization process. However, obtaining a large and diverse
dataset to train such a classifier that works well
across many types of medical text poses a challenge as privacy laws prohibit the sharing of
raw medical records. We introduce a method
to create privacy-preserving shareable representations of medical text (i.e. they contain
no PHI) that does not require expensive manual pseudonymization. These representations
can be shared between organizations to create
unified datasets for training de-identification
models. Our representation allows training a
simple LSTM-CRF de-identification model to
an F1 score of 97.4%, which is comparable
to a strong baseline that exposes private information in its representation. A robust, widely
available de-identification classifier based on
our representation could potentially enable
studies for which de-identification would otherwise be too costly