ArticleJournal of biomedical informatics2015
Automatic detection of protected health information from clinic narratives.
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 36 papers.
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.
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Who cites it
36 citing papers in PubMed.
- MedADL: high-throughput information extraction of functional status from electronic health records to advance frailty assessment in older adults.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Article
- Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.Applied clinical informatics · 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
- Named Entity Recognition in Electronic Health Records: A Methodological Review.Healthcare informatics research · 2023Article
- Not so weak PICO: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation.JAMIA open · 2023Article
- Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- De-identification of free text data containing personal health information: a scoping review of reviews.International journal of population data science · 2023Article
- A Context-Enhanced De-identification System.ACM transactions on computing for healthcare · 2022Article
- Article
- A Novel COVID-19 Data Set and an Effective Deep Learning Approach for the De-Identification of Italian Medical Records.IEEE access : practical innovations, open solutions · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Using word embeddings to improve the privacy of clinical notes.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Comparative Study of Various Approaches for Ensemble-based De-identification of Electronic Health Record Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- A study of deep learning methods for de-identification of clinical notes in cross-institute settings.BMC medical informatics and decision making · 2019Article
- Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study.JMIR medical informatics · 2019Article
- A Feature-Driven Decision Support System for Heart Failure Prediction Based onComputational and mathematical methods in medicine · 2019Article
- Healthcare Data Breaches: Implications for Digital Forensic Readiness.Journal of medical systems · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This paper presents a natural language processing (NLP) system that was designed to participate in the 2014 i2b2 de-identification challenge. The challenge task aims to identify and classify seven main Protected Health Information (PHI) categories and 25 associated sub-categories. A hybrid model was proposed which combines machine learning techniques with keyword-based and rule-based approaches to deal with the complexity inherent in PHI categories. Our proposed approaches exploit a rich set of linguistic features, both syntactic and word surface-oriented, which are further enriched by task-specific features and regular expression template patterns to characterize the semantics of various PHI categories. Our system achieved promising accuracy on the challenge test data with an overall micro-averaged F-measure of 93.6%, which was the winner of this de-identification challenge.
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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.