ArticleProceedings of the ACM Conference on Health, Inference, and Learning2020
Deidentification of free-text medical records using pre-trained bidirectional transformers.
Article in Proceedings of the ACM Conference on Health, Inference, and Learning, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Potential of Research Drawing on Clinical Free Text to Bring Benefits to Patients in the United Kingdom: A Systematic Review of the Literature.Frontiers in digital health · 2021Pooled it
- MAP: evaluation and multi-agent enhancement of large language models for inpatient pathways.npj health systems · 2026Article
- MedVidDeID: Protecting privacy in clinical encounter video recordings.Journal of biomedical informatics · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
- De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.BMC medical informatics and decision making · 2025Article
- A Transformer-Based Pipeline for German Clinical Document De-Identification.Applied clinical informatics · 2025Article
- Guardians of the data: NER and LLMs for effective medical record anonymization in Brazilian Portuguese.Frontiers in public health · 2025Article
- Automated redaction of names in adverse event reports using transformer-based neural networks.BMC medical informatics and decision making · 2024Article
- Transformer models in biomedicine.BMC medical informatics and decision making · 2024Review
- Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.Applied clinical informatics · 2024Article
- Web-Based Application Based on Human-in-the-Loop Deep Learning for Deidentifying Free-Text Data in Electronic Medical Records: Development and Usability Study.Interactive journal of medical research · 2023Article
- Automated deidentification of radiology reports combining transformer and "hide in plain sight" rule-based methods.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems.Frontiers in medicine · 2023Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Deidentification of free-text medical records using pre-trained bidirectional transformers.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- Benchmarking Modern Named Entity Recognition Techniques for Free-text Health Record Deidentification.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational ScienceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The ability of caregivers and investigators to share patient data is fundamental to many areas of clinical practice and biomedical research. Prior to sharing, it is often necessary to remove identifiers such as names, contact details, and dates in order to protect patient privacy. Deidentification, the process of removing identifiers, is challenging, however. High-quality annotated data for developing models is scarce; many target identifiers are highly heterogenous (for example, there are uncountable variations of patient names); and in practice anything less than perfect sensitivity may be considered a failure. As a result, patient data is often withheld when sharing would be beneficial, and identifiable patient data is often divulged when a deidentified version would suffice. In recent years, advances in machine learning methods have led to rapid performance improvements in natural language processing tasks, in particular with the advent of large-scale pretrained language models. In this paper we develop and evaluate an approach for deidentification of clinical notes based on a bidirectional transformer model. We propose human interpretable evaluation measures and demonstrate state of the art performance against modern baseline models. Finally, we highlight current challenges in deidentification, including the absence of clear annotation guidelines, lack of portability of models, and paucity of training data. Code to develop our model is open source, allowing for broad reuse.
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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.