Beyond Accuracy: Automated De-Identification of Large Real-World Clinical Text Datasets
Abstract
Recent research advances achieve human-level accuracy for de-identifying free-text clinical notes on research datasets, but gaps remain in reproducing this in large real-world settings. This paper summarizes lessons learned from building a system used to de-identify over one billion real clinical notes, in a fully automated way, that was independently certified by multiple organizations for production use. A fully automated solution requires a very high level of accuracy that does not require manual review. A hybrid context-based model architecture is described, which outperforms a Named Entity Recogniton (NER) - only model by 10% on the i2b2-2014 benchmark. The proposed system makes 50%, 475%, and 575% fewer errors than the comparable AWS, Azure, and GCP services respectively while also outperforming ChatGPT by 33%. It exceeds 98% coverage of sensitive data across 7 European languages, without a need for fine tuning. A second set of described models enable data obfuscation -- replacing sensitive data with random surrogates -- while retaining name, date, gender, clinical, and format consistency. Both the practical need and the solution architecture that provides for reliable & linked anonymized documents are described.
- Publication:
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arXiv e-prints
- Pub Date:
- December 2023
- DOI:
- 10.48550/arXiv.2312.08495
- arXiv:
- arXiv:2312.08495
- Bibcode:
- 2023arXiv231208495K
- Keywords:
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- Computer Science - Computation and Language;
- Computer Science - Cryptography and Security;
- Computer Science - Machine Learning
- E-Print:
- Extended Abstract presented at Machine Learning for Health (ML4H) symposium 2023, December 10th, 2023, New Orleans, United States, 13 pages