ArticleJournal of biomedical informatics2017
De-identification of clinical notes via recurrent neural network and conditional random field.
Article 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 45 papers, 1 of them a synthesis that pooled it.
What it found
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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
45 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
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- Clinical Assessment of Fine-Tuned Open-Source LLMs in Cardiology: From Progress Notes to Discharge Summary.Journal of healthcare informatics research · 2025Article
- A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.Journal of the American Medical Informatics Association : JAMIA · 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
- 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
- Man vs the machine in the struggle for effective text anonymisation in the age of large language models.Scientific reports · 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
- Clinical concept and relation extraction using prompt-based machine reading comprehension.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A cross-institutional evaluation on breast cancer phenotyping NLP algorithms on electronic health records.Computational and structural biotechnology journal · 2023Article
- A Context-Enhanced De-identification System.ACM transactions on computing for healthcare · 2022Article
- Enhanced neurologic concept recognition using a named entity recognition model based on transformers.Frontiers in digital health · 2022Article
- Classifying Cyber-Risky Clinical Notes by Employing Natural Language Processing.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2022Article
- Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud.Yonsei medical journal · 2022Article
- Transferability of neural network clinical deidentification systems.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Improving domain adaptation in de-identification of electronic health records through self-training.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
De-identification, identifying information from data, such as protected health information (PHI) present in clinical data, is a critical step to enable data to be shared or published. The 2016 Centers of Excellence in Genomic Science (CEGS) Neuropsychiatric Genome-scale and RDOC Individualized Domains (N-GRID) clinical natural language processing (NLP) challenge contains a de-identification track in de-identifying electronic medical records (EMRs) (i.e., track 1). The challenge organizers provide 1000 annotated mental health records for this track, 600 out of which are used as a training set and 400 as a test set. We develop a hybrid system for the de-identification task on the training set. Firstly, four individual subsystems, that is, a subsystem based on bidirectional LSTM (long-short term memory, a variant of recurrent neural network), a subsystem-based on bidirectional LSTM with features, a subsystem based on conditional random field (CRF) and a rule-based subsystem, are used to identify PHI instances. Then, an ensemble learning-based classifiers is deployed to combine all PHI instances predicted by above three machine learning-based subsystems. Finally, the results of the ensemble learning-based classifier and the rule-based subsystem are merged together. Experiments conducted on the official test set show that our system achieves the highest micro F1-scores of 93.07%, 91.43% and 95.23% under the "token", "strict" and "binary token" criteria respectively, ranking first in the 2016 CEGS N-GRID NLP challenge. In addition, on the dataset of 2014 i2b2 NLP challenge, our system achieves the highest micro F1-scores of 96.98%, 95.11% and 98.28% under the "token", "strict" and "binary token" criteria respectively, outperforming other state-of-the-art systems. All these experiments prove the effectiveness of our proposed method.
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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.