PyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.

ABSTRACT:

Objectives

Deidentification of personally identifiable information in free-text clinical data is fundamental to making these data broadly available for research. However, there exist gaps in the deidentification landscape with regard to the functionality and flexibility of extant tools, as well as suboptimal tradeoffs between deidentification accuracy and speed. To address these gaps and tradeoffs, we develop a new Python-based deidentification software, pyDeid.

Materials and methods

pyDeid uses a combination of regular expression-based rules, fixed exclusion lists and inclusion lists to deidentify free-text data. Additional configurations of pyDeid include optional named entity recognition and custom name lists. We measure its deidentification performance and speed on 700 a

SUBMITTER: Sundrelingam V 

PROVIDER: S-EPMC11752853 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature