ArticleJournal of biomedical informatics2015
Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 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
71 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithms to anonymize structured medical and healthcare data: A systematic review.Frontiers in bioinformatics · 2022Pooled it
- Anonymization of Portuguese Clinical Notes Using Large Language Models and Quantum-Enhanced Hybrid Architectures: Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- Memorization in large language models in medicine prevalence characteristics and implications.Nature communications · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Toward Automating the Summarization of Cancer Pathology Reports Using Large Language Models to Improve Clinical Usability.JCO clinical cancer informatics · 2026Article
- Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models.Journal of medical Internet research · 2025Article
- Benchmarking transformer-based models for medical record de-identification in a single center multi-specialty evaluation.iScience · 2025Article
- A Markov Chain Replacement Strategy for Surrogate Identifiers: Minimizing Re-Identification Risk While Preserving Text Reuse.Electronics · 2025Article
- Machine learning in psychiatric health records: A gold standard approach to trauma annotation.Translational psychiatry · 2025Article
- Automated anonymization of radiology reports: comparison of publicly available natural language processing and large language models.European radiology · 2025Article
- pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.JAMIA open · 2025Article
- Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Automated redaction of names in adverse event reports using transformer-based neural networks.BMC medical informatics and decision making · 2024Article
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- On the development and validation of large language model-based classifiers for identifying social determinants of health.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.Applied clinical informatics · 2024Article
- Clinical natural language processing for secondary uses.Journal of biomedical informatics · 2024Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on the de-identification of longitudinal medical records. For this track, we de-identified a set of 1304 longitudinal medical records describing 296 patients. This corpus was de-identified under a broad interpretation of the HIPAA guidelines using double-annotation followed by arbitration, rounds of sanity checking, and proof reading. The average token-based F1 measure for the annotators compared to the gold standard was 0.927. The resulting annotations were used both to de-identify the data and to set the gold standard for the de-identification track of the 2014 i2b2/UTHealth shared task. All annotated private health information were replaced with realistic surrogates automatically and then read over and corrected manually. The resulting corpus is the first of its kind made available for de-identification research. This corpus was first used for the 2014 i2b2/UTHealth shared task, during which the systems achieved a mean F-measure of 0.872 and a maximum F-measure of 0.964 using entity-based micro-averaged evaluations.
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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.