pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records
pyDeid allows the flexibility to prioritize between performance and speed, as well as precision and recall, while addressing some of the gaps in functionality left by other tools. pyDeid is also generalizable to domains outside of clinical data and can be further customized for specific contexts or for particular workflows.
Authors
- Fahad A. Razak (ORCID: https://orcid.org/0000-0001-9195-7286)
- Amol A. Verma (ORCID: https://orcid.org/0000-0001-6857-931X)
- Radha Koppula
- Surain B. Roberts (ORCID: https://orcid.org/0000-0001-8792-2240)
- Frances Pogacar
- Saeha Shin (ORCID: https://orcid.org/0000-0001-7564-6963)
- Vaakesan Sundrelingam
- Chloé Pou-Prom (ORCID: https://orcid.org/0000-0002-5719-7786)
- Shireen Parimoo
Institutions
- St. Michael's Hospital (CA)
- University of Toronto (CA)
- Hospital for Sick Children (CA)
- Unity Health System (US)
- Health Solutions (Sweden) (SE)
- St Michaels Hospital (GB)
Publication Details
- Journal
- JAMIA Open
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/jamiaopen/ooae152
- Citations
- 2
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- Canadian Cancer Society
- Canadian Frailty Network
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada