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OpenDeID Pipeline for Unstructured Electronic Health Record Text Notes Based on Rules and Transformers: Deidentification Algorithm Development and Validation Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.cmpbup.2021.100024openalex-snapshot
Cohort selection for construction of a clinical natural language processing corpus
inferredcites →https://doi.org/10.1016/j.jbi.2014.01.014openalex-snapshot
Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research
inferredcites →https://doi.org/10.1016/j.jbi.2015.06.007openalex-snapshot
Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
inferredcites →https://doi.org/10.1016/j.jbi.2015.06.029openalex-snapshot
Combining knowledge- and data-driven methods for de-identification of clinical narratives
inferredcites →https://doi.org/10.1016/j.jbi.2015.08.012openalex-snapshot
CRFs based de-identification of medical records
inferredcites →https://doi.org/10.1016/j.jbi.2017.05.023openalex-snapshot
De-identification of clinical notes via recurrent neural network and conditional random field
inferredcites →https://doi.org/10.1016/j.jbi.2017.06.006openalex-snapshot
A hybrid approach to automatic de-identification of psychiatric notes
inferredcites →https://doi.org/10.1016/j.jbi.2017.06.011openalex-snapshot
De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1
inferredcites →https://doi.org/10.1016/j.jbi.2022.104215openalex-snapshot
De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models
inferredcites →https://doi.org/10.1016/j.patter.2021.100255openalex-snapshot
Building a best-in-class automated de-identification tool for electronic health records through ensemble learning
inferredcites →https://doi.org/10.1038/s41598-020-75544-1openalex-snapshot
De-identification of electronic health record using neural network
inferredcites →https://doi.org/10.1038/s41598-021-99554-9openalex-snapshot
The OpenDeID corpus for patient de-identification
inferredcites →https://doi.org/10.1038/s41746-020-0258-yopenalex-snapshot
Protected Health Information filter (Philter): accurately and securely de-identifying free-text clinical notes
inferredcites →https://doi.org/10.1093/bioinformatics/btz682openalex-snapshot
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
inferredcites →https://doi.org/10.1093/med/9780190852917.003.0015openalex-snapshot
Electronic Health Records
inferredcites →https://doi.org/10.1097/md.0000000000003332openalex-snapshot
Electronic Health Records and Quality of Care
inferredcites →https://doi.org/10.1136/amiajnl-2012-001020openalex-snapshot
BoB, a best-of-breed automated text de-identification system for VHA clinical documents
inferredcites →https://doi.org/10.1145/3443279.3443304openalex-snapshot
A Comparative Study on Word Embeddings in Deep Learning for Text Classification
inferredcites →https://doi.org/10.1184/r1/6625565openalex-snapshot
Replacing Personally-Identifying Information in Medical Records, the Scrub System
inferredcites →https://doi.org/10.1186/1471-2105-7-s5-s11openalex-snapshot
NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition
inferredcites →https://doi.org/10.1186/1471-2288-10-70openalex-snapshot
Automatic de-identification of textual documents in the electronic health record: a review of recent research
inferredcites →https://doi.org/10.1186/1472-6947-13-s1-s1openalex-snapshot
Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
inferredcites →https://doi.org/10.1186/1472-6947-8-32openalex-snapshot
Automated de-identification of free-text medical records
inferredcites →https://doi.org/10.1186/s12911-018-0598-6openalex-snapshot
Leveraging text skeleton for de-identification of electronic medical records
inferredcites →https://doi.org/10.1186/s12911-019-0935-4openalex-snapshot
A study of deep learning methods for de-identification of clinical notes in cross-institute settings
inferredcites →https://doi.org/10.1197/jamia.m2444openalex-snapshot
Evaluating the State-of-the-Art in Automatic De-identification
inferredcites →https://doi.org/10.1309/e6k3-3gbp-e5c2-7fyuopenalex-snapshot
Evaluation of a Deidentification (De-Id) Software Engine to Share Pathology Reports and Clinical Documents for Research
inferredcites →https://doi.org/10.2196/17622openalex-snapshot
Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation
inferredcites →https://doi.org/10.3115/v1/d14-1162openalex-snapshot
Glove: Global Vectors for Word Representation
inferredcites →https://doi.org/10.3233/shti190185openalex-snapshot
Generation of Surrogates for De-Identification of Electronic Health Records
inferredcites →https://doi.org/10.3390/s22114157openalex-snapshot
Transfer Learning for Sentiment Analysis Using BERT Based Supervised Fine-Tuning
inferredcites →https://doi.org/10.4018/978-1-4666-9432-3openalex-snapshot
Improving Health Management through Clinical Decision Support Systems
inferredcites →https://doi.org/10.4018/978-1-4666-9432-3.ch012openalex-snapshot
Mining Electronic Health Records to Guide and Support Clinical Decision Support Systems
inferredcites →https://doi.org/10.4018/978-1-7998-0951-7.ch048openalex-snapshot
Patient Data De-Identification
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.48550/arxiv.1810.01570openalex-snapshot
A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation
inferredcites →https://doi.org/10.48550/arxiv.1908.03548openalex-snapshot
BERT-based Ranking for Biomedical Entity Normalization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Architectural Separation for Privacy-Preserving LLM-Assisted Nursing Handovers in Resource-Constrained Healthcare
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_1openalex-snapshot
Deidentification and Temporal Normalization of the Electronic Health Record Notes Using Large Language Models: The 2023 SREDH/AI-Cup Competition for Deidentification of Sensitive Health Information
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A Hybrid Approach to the Recognition of Sensitive Health Information: LLM and Regular Expressions
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Patient Privacy Information Retrieval with Longformer and CRF, Followed by Rule-Based Time Information Normalization: A Dual-Approach Study
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A Two-Stage Fine-Tuning Procedure to Improve the Performance of Language Models in Sensitive Health Information Recognition and Normalization Tasks
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_2openalex-snapshot
Enhancing Automated De-identification of Pathology Text Notes Using Pre-trained Language Models
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_3openalex-snapshot
A Comparative Study of GPT3.5 Fine Tuning and Rule-Based Approaches for De-identification and Normalization of Sensitive Health Information in Electronic Medical Record Notes
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_4openalex-snapshot
Advancing Sensitive Health Data Recognition and Normalization Through Large Language Model Driven Data Augmentation
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_5openalex-snapshot
Privacy Protection and Standardization of Electronic Medical Records Using Large Language Model
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_6openalex-snapshot
Applying Language Models for Recognizing and Normalizing Sensitive Information from Electronic Health Records Text Notes
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_7openalex-snapshot
Enhancing SHI Extraction and Time Normalization in Healthcare Records Using LLMs and Dual-Model Voting
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_8openalex-snapshot
Evaluation of OpenDeID Pipeline in the 2023 SREDH/AI-Cup Competition for Deidentification of Sensitive Health Information
inferred← citeshttps://doi.org/10.1007/978-981-97-7966-6_9openalex-snapshot
Sensitive Health Information Extraction from EMR Text Notes: A Rule-Based NER Approach Using Linguistic Contextual Analysis
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2025.106225openalex-snapshot
De-identification of clinical data: A systematic review of free text, image and tabular data approaches
inferred← citeshttps://doi.org/10.1038/s41746-025-01429-0openalex-snapshot
Privacy preserving strategies for electronic health records in the era of large language models
inferred← citeshttps://doi.org/10.1038/s41746-025-01921-7openalex-snapshot
Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records
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Towards Real-Time Anonymization of Unstructured Data with Deep Learning: A Privacy-Preserving Framework Prototype
inferred← citeshttps://doi.org/10.1109/icrito61523.2024.10522392openalex-snapshot
Enhancing Educational Interactions: A Comprehensive Review of AI Chatbots in Learning Environments
inferred← citeshttps://doi.org/10.1136/bmjhci-2025-101894openalex-snapshot
Benchmarking large language models for de-identification of electronic health record notes
inferred← citeshttps://doi.org/10.1146/annurev-biodatasci-092724-031932openalex-snapshot
Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks
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Remote monitoring system for older adults at risk for complications: a scoping review
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A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions
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A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions
inferred← citeshttps://doi.org/10.2196/57828openalex-snapshot
Harnessing Moderate-Sized Language Models for Reliable Patient Data Deidentification in Emergency Department Records: Algorithm Development, Validation, and Implementation Study
inferred← citeshttps://doi.org/10.2196/70940openalex-snapshot
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Scalable and Privacy-Conscious End-to-End Processing of Large-Scale Clinical Data for Precision Medicine: Empirical Evaluation Study
inferred← citeshttps://doi.org/10.2196/93950openalex-snapshot
Mapping Machine Learning–Driven Cybersecurity Solutions in Health Care: Scoping Literature Review
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Harnessing Moderate-Sized Language Models for Reliable Patient Data De-identification in Emergency Department Records: An Evaluation of Strategies and Performance (Preprint)
inferred← citeshttps://doi.org/10.3233/shti240515openalex-snapshot
Preliminary Evaluation of Fine-Tuning the OpenDeLD Deidentification Pipeline Across Multi-Center Corpora
inferred← citeshttps://doi.org/10.3390/bioengineering11040342openalex-snapshot
Applications of Large Language Models in Pathology
inferred← citeshttps://doi.org/10.3390/info16060489openalex-snapshot
A Systematic Review of Large Language Models in Medical Specialties: Applications, Challenges and Future Directions
inferred← citeshttps://doi.org/10.3390/j9030022openalex-snapshot
Clinical Text De-Identification Beyond PHI Detection: A Scoping Review
inferred← citeshttps://doi.org/10.59681/2175-4411.v17.2025.1227openalex-snapshot
Anonimização de textos médicos com processamento de linguagem natural
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