ArticleJournal of Korean medical science2015
A De-identification method for bilingual clinical texts of various note types.
Article in Journal of Korean medical science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
41 citing papers in PubMed.
- Domain and Language adaptive pre-training of BERT models for Korean-English bilingual clinical text analysis.BMC medical informatics and decision making · 2025Article
- Protecting medical privacy with pretrained language models: named entity recognition-based de-identification in Korean unstructured electronic medical records.npj health systems · 2025Article
- De-identification of clinical notes with pseudo-labeling using regular expression rules and pre-trained BERT.BMC medical informatics and decision making · 2025Article
- Using Large Language Models to Extract Core Injury Information From Emergency Department Notes.Journal of Korean medical science · 2024Article
- Conditional diagnostic accuracy according to inflammation status and age for diagnosing tuberculous effusion.BMC pulmonary medicine · 2023Article
- Development of an artificial intelligence bacteremia prediction model and evaluation of its impact on physician predictions focusing on uncertainty.Scientific reports · 2023Article
- De-identifying Norwegian Clinical Text using Resources from Swedish and Danish.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Automated extraction of information of lung cancer staging from unstructured reports of PET-CT interpretation: natural language processing with deep-learning.BMC medical informatics and decision making · 2022Article
- Classification of pleural effusions using deep learning visual models: contrastive-loss.Scientific reports · 2022Article
- Protected Health Information Recognition by Fine-Tuning a Pre-training Transformer Model.Healthcare informatics research · 2022Article
- Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians.Journal of Korean medical science · 2021Review
- Contralateral Suppression at Adrenal Venous Sampling Is Associated with Renal Impairment Following Adrenalectomy for Unilateral Primary Aldosteronism.Endocrinology and metabolism (Seoul, Korea) · 2021Observational
- Evaluation of the Privacy Risks of Personal Health Identifiers and Quasi-Identifiers in a Distributed Research Network: Development and Validation Study.JMIR medical informatics · 2021Article
- An Attention Model With Transfer Embeddings to Classify Pneumonia-Related Bilingual Imaging Reports: Algorithm Development and Validation.JMIR medical informatics · 2021Article
- Diagnostic Accuracy of Computed Tomography in Predicting Primary Aldosteronism Subtype According to Age.Endocrinology and metabolism (Seoul, Korea) · 2021Article
- A Framework (SOCRATex) for Hierarchical Annotation of Unstructured Electronic Health Records and Integration Into a Standardized Medical Database: Development and Usability Study.JMIR medical informatics · 2021Article
- Primary aldosteronism subtyping in the setting of partially successful adrenal vein sampling.Therapeutic advances in endocrinology and metabolism · 2021Article
- Effects of the duration of bridge to lung transplantation with extracorporeal membrane oxygenation.PloS one · 2021Article
- Evaluating waiting time with real-world health information in a high-volume cancer center.Medicine · 2020Observational
- Detection of Bacteremia in Surgical In-Patients Using Recurrent Neural Network Based on Time Series Records: Development and Validation Study.Journal of medical Internet research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
De-identification of personal health information is essential in order not to require written patient informed consent. Previous de-identification methods were proposed using natural language processing technology in order to remove the identifiers in clinical narrative text, although these methods only focused on narrative text written in English. In this study, we propose a regular expression-based de-identification method used to address bilingual clinical records written in Korean and English. To develop and validate regular expression rules, we obtained training and validation datasets composed of 6,039 clinical notes of 20 types and 5,000 notes of 33 types, respectively. Fifteen regular expression rules were constructed using the development dataset and those rules achieved 99.87% precision and 96.25% recall for the validation dataset. Our de-identification method successfully removed the identifiers in diverse types of bilingual clinical narrative texts. This method will thus assist physicians to more easily perform retrospective research.
Indexed as
Identifiers
What OpenQuestion holds
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.