ArticleJournal of biomedical informatics2017
A hybrid approach to automatic de-identification of psychiatric notes.
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 21 papers, 1 of them a synthesis that pooled it.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Findings from 2017 on Health Information ManagementYearbook of medical informatics · 2018Pooled it
- Language-informed treatment recommendations in psychiatry: bridging the gap in individual predictability.Frontiers in psychiatry · 2026Article
- pyDeid: an improved, fast, flexible, and generalizable rule-based approach for deidentification of free-text medical records.JAMIA open · 2025Article
- Evaluating GPT models for clinical note de-identification.Scientific reports · 2025Article
- 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
- 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
- De-identification of free text data containing personal health information: a scoping review of reviews.International journal of population data science · 2023Article
- 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
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Re-examination of Rule-Based Methods in Deidentification of Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2020Article
- Deidentification of free-text medical records using pre-trained bidirectional transformers.Proceedings of the ACM Conference on Health, Inference, and Learning · 2020Article
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Customization scenarios for de-identification of clinical notes.BMC medical informatics and decision making · 2020Article
- A study of deep learning methods for de-identification of clinical notes in cross-institute settings.BMC medical informatics and decision making · 2019Article
- Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.Journal of biomedical informatics · 2017Review
- A natural language processing challenge for clinical records: Research Domains Criteria (RDoC) for psychiatry.Journal of biomedical informatics · 2017Article
- Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
De-identification, or identifying and removing protected health information (PHI) from clinical data, is a critical step in making clinical data available for clinical applications and research. This paper presents a natural language processing system for automatic de-identification of psychiatric notes, which was designed to participate in the 2016 CEGS N-GRID shared task Track 1. The system has a hybrid structure that combines machine leaning techniques and rule-based approaches. The rule-based components exploit the structure of the psychiatric notes as well as characteristic surface patterns of PHI mentions. The machine learning components utilize supervised learning with rich features. In addition, the system performance was boosted with integration of additional data to the training set through domain adaptation. The hybrid system showed overall micro-averaged F-score 90.74 on the test set, second-best among all the participants of the CEGS N-GRID task.
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.