Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts
Journal:
arXiv
Published Date:
Feb 18, 2025
Abstract
Sharing sensitive texts for scientific purposes requires appropriate
techniques to protect the privacy of patients and healthcare personnel.
Anonymizing textual data is particularly challenging due to the presence of
diverse unstructured direct and indirect identifiers. To mitigate the risk of
re-identification, this work introduces a schema of nine categories of indirect
identifiers designed to account for different potential adversaries, including
acquaintances, family members and medical staff. Using this schema, we annotate
100 MIMIC-III discharge summaries and propose baseline models for identifying
indirect identifiers. We will release the annotation guidelines, annotation
spans (6,199 annotations in total) and the corresponding MIMIC-III document IDs
to support further research in this area.
