A study of deep learning methods for de-identification of clinical notes in cross-institute settings
Open accessAbstract
Background: De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions. Methods: We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources. Results: Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively. Conclusions: It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.
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We screened 4 different word embeddings and found that the Common Crawl embeddings achieved better performance compared to other embeddings on a validation data set.
“We screened 4 different word embeddings following a similar procedure reported in our previous study [46] and found that the Common Crawl embeddings—released by Facebook and trained using the fastText on the Common Crawl data set [47]—achieved better performance compared to other embeddings on a validation data set.”
From Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models (2020) · cites this paper as [46]
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Deep Learning Models for Automatic De-identification of Clinical Text
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- Authors
- Yang, X., Lyu, T., Li, Q., Lee, C.-Y., Bian, J., Hogan, W. R., & Wu, Y.
- Affiliations
- University of Florida Health (US)
- Corresponding author
- Wu, Yonghui
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2019-12
- Volume
- 19
- Issue
- S5
- Pages
- 232-232
- ISSN
- 1472-6947 (ISSN-L)
- PMC
- PMC6894104
- OpenAlex
- W2993961432
- Cited by
- 73
- References
- 42
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 9 · 2023: 13 · 2022: 16 · 2021: 7 · 2020: 9 · 2019: 1
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Informatics and Decision Making (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Nvidia
- University of Florida (UL1 TR001427)
- National Institutes of Health (UL1TR001427, UL1TR000064)
- Clinical and Translational Science Institute, University of Florida (UL1TR001427)
- National Institute on Aging (P30AG028740)
- National Center for Advancing Translational Sciences
- Moonshot Research and Development Program
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- clinical notes, deep learning, word embeddings, pre-trained model fine-tuning, patient privacy, clinical text
- MeSH terms
- Data Anonymization, Deep Learning, Confidentiality, Humans, Linguistics, Natural Language Processing, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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