aphp/eds-pseudo

EDS-Pseudo is a hybrid model for detecting personally identifying entities in clinical reports

48
/ 100
Emerging

This tool helps healthcare professionals and researchers anonymize sensitive patient data within clinical reports. It takes raw clinical text and identifies elements like names, dates, addresses, and hospital identifiers, then removes or replaces them. This ensures patient privacy when sharing data for research or analysis.

Use this if you need to quickly and accurately remove personally identifying information from large volumes of clinical documents to comply with privacy regulations or enable data sharing.

Not ideal if your documents are not clinical reports or if you need to pseudonymize very specialized types of identifying data not covered by the listed categories.

clinical-data-anonymization healthcare-privacy medical-research-data patient-confidentiality clinical-text-processing
No Package No Dependents
Maintenance 10 / 25
Adoption 8 / 25
Maturity 16 / 25
Community 14 / 25

How are scores calculated?

Stars

67

Forks

9

Language

Python

License

Last pushed

Feb 05, 2026

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/nlp/aphp/eds-pseudo"

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