ArticleBMC medical informatics and decision making2020
Customization scenarios for de-identification of clinical notes.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
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Who cites it
14 citing papers in PubMed.
- HUPA: A corpus of disordered and normophonic voices in Castilian Spanish.Data in brief · 2026Review
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.BMC medical informatics and decision making · 2025Article
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
- Lightweight transformers for clinical natural language processing.Natural language engineering · 2024Article
- Development and Validation of a Natural Language Processing Algorithm to Pseudonymize Documents in the Context of a Clinical Data Warehouse.Methods of information in medicine · 2024Article
- Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.Applied clinical informatics · 2024Article
- Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Commercializing Personal Health Information: A Critical Qualitative Content Analysis of Documents Describing Proprietary Primary Care Databases in Canada.International journal of health policy and management · 2023Article
- Selecting Privacy-Enhancing Technologies for Managing Health Data Use.Frontiers in public health · 2022Review
- Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians.Journal of Korean medical science · 2021Review
- The OpenDeID corpus for patient de-identification.Scientific reports · 2021Article
- Improving domain adaptation in de-identification of electronic health records through self-training.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Deidentification of free-text medical records using pre-trained bidirectional transformers.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAutomated machine-learning systems are able to de-identify electronic medical records, including free-text clinical notes. Use of such systems would greatly boost the amount of data available to researchers, yet their deployment has been limited due to uncertainty about their performance when applied to new datasets.
objectiveWe present practical options for clinical note de-identification, assessing performance of machine learning systems ranging from off-the-shelf to fully customized.
methodsWe implement a state-of-the-art machine learning de-identification system, training and testing on pairs of datasets that match the deployment scenarios. We use clinical notes from two i2b2 competition corpora, the Physionet Gold Standard corpus, and parts of the MIMIC-III dataset.
resultsFully customized systems remove 97-99% of personally identifying information. Performance of off-the-shelf systems varies by dataset, with performance mostly above 90%. Providing a small labeled dataset or large unlabeled dataset allows for fine-tuning that improves performance over off-the-shelf systems.
conclusionHealth organizations should be aware of the levels of customization available when selecting a de-identification deployment solution, in order to choose the one that best matches their resources and target performance level.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.