ArticleJournal of the American Medical Informatics Association : JAMIA2017
De-identification of patient notes with recurrent neural networks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 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
99 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Anonymization of Portuguese Clinical Notes Using Large Language Models and Quantum-Enhanced Hybrid Architectures: Comparative Evaluation Study.Journal of medical Internet research · 2026Article
- Bridging the performance gap: systematic optimization of local LLMs for Japanese medical PHI extraction.Scientific reports · 2026Article
- Digitising health history: The creation, function and implementation of the Norwegian Health Archives Registry.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- Clinical Assessment of Fine-Tuned Open-Source LLMs in Cardiology: From Progress Notes to Discharge Summary.Journal of healthcare informatics research · 2025Article
- A Markov Chain Replacement Strategy for Surrogate Identifiers: Minimizing Re-Identification Risk While Preserving Text Reuse.Electronics · 2025Article
- MedVidDeID: Protecting privacy in clinical encounter video recordings.Journal of biomedical informatics · 2025Article
- A hybrid rule-based NLP and machine learning approach for PII detection and anonymization in financial documents.Scientific reports · 2025Article
- Article
- 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
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
- Synthetic4Health: generating annotated synthetic clinical letters.Frontiers in digital health · 2025Article
- Automated redaction of names in adverse event reports using transformer-based neural networks.BMC medical informatics and decision making · 2024Article
- Building an Ethical and Trustworthy Biomedical AI Ecosystem for the Translational and Clinical Integration of Foundation Models.Bioengineering (Basel, Switzerland) · 2024Review
- An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.Journal of medical Internet research · 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
- Automatic de-identification of French electronic health records: a cost-effective approach exploiting distant supervision and deep learning models.BMC medical informatics and decision making · 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
- Man vs the machine in the struggle for effective text anonymisation in the age of large language models.Scientific reports · 2023Article
- Unveiling the Enigma: Exploring Risk Factors and Mechanisms for Psychotic Symptoms in Alzheimer's Disease through Electronic Medical Records with Deep Learning Models.Pharmaceuticals (Basel, Switzerland) · 2023Article
39 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePatient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of electronic health record databases, the limited number of researchers with access to non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. MATERIALS AND
methodsWe introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset.
resultsOur ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall of 97.38 and a precision of 98.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall of 99.25 and a precision of 99.21.
conclusionOur findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no manual feature engineering.
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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.