Using word embeddings to improve the privacy of clinical notes.

ABSTRACT:

Objective

In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current techniques).

Materials and methods

We employ a new "random replacement" paradigm (replacing each token in clinical notes with neighboring word vectors from the embedding space) to achieve 100% recall on the removal of sensitive information, unachievable with current "search-and-secure" paradigms. We demonstrate the utility of this paradigm on multiple corpora in a diverse set of classification tasks.

Results

We empirically evaluate the effect of our anonymization technique both on upstream and downstream natural language processing

SUBMITTER: Abdalla M 

PROVIDER: S-EPMC7309261 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature