ReviewJournal of biomedical informatics2017
De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.
Review in Journal of biomedical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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.
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Who cites it
43 citing papers in PubMed.
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- ASQ-PHI: An adversarial synthetic data benchmark for clinical de-identification and search utility.Data in brief · 2026Article
- Bridging the performance gap: systematic optimization of local LLMs for Japanese medical PHI extraction.Scientific reports · 2026Article
- Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records.NPJ digital medicine · 2025Article
- An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.Journal of medical Internet research · 2024Article
- Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study.Journal of medical Internet research · 2024Article
- OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study.Journal of medical Internet research · 2023Article
- 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
- De-identification of free text data containing personal health information: a scoping review of reviews.International journal of population data science · 2023Article
- A scoping review of publicly available language tasks in clinical natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2022Article
- A Context-Enhanced De-identification System.ACM transactions on computing for healthcare · 2022Article
- Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing.Frontiers in allergy · 2022Article
- Transferability of neural network clinical deidentification systems.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- The OpenDeID corpus for patient de-identification.Scientific reports · 2021Article
- Article
- Using word embeddings to improve the privacy of clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Deidentification of free-text medical records using pre-trained bidirectional transformers.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Ensemble method-based extraction of medication and related information from clinical texts.Journal of the American Medical Informatics Association : JAMIA · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The 2016 CEGS N-GRID shared tasks for clinical records contained three tracks. Track 1 focused on de-identification of a new corpus of 1000 psychiatric intake records. This track tackled de-identification in two sub-tracks: Track 1.A was a "sight unseen" task, where nine teams ran existing de-identification systems, without any modifications or training, on 600 new records in order to gauge how well systems generalize to new data. The best-performing system for this track scored an F1 of 0.799. Track 1.B was a traditional Natural Language Processing (NLP) shared task on de-identification, where 15 teams had two months to train their systems on the new data, then test it on an unannotated test set. The best-performing system from this track scored an F1 of 0.914. The scores for Track 1.A show that unmodified existing systems do not generalize well to new data without the benefit of training data. The scores for Track 1.B are slightly lower than the 2014 de-identification shared task (which was almost identical to 2016 Track 1.B), indicating that these new psychiatric records pose a more difficult challenge to NLP systems. Overall, de-identification is still not a solved problem, though it is important to the future of clinical NLP.
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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.