ArticlePatterns (New York, N.Y.)2021
Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.
Article in Patterns (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed.
- Higher semaglutide dose is associated with lower neuropsychiatric event incidence independent of weight loss.npj metabolic health and disease · 2026Article
- Privacy and Security Throughout the Health Data Life Cycle: From Primary Care to Research Networks.Annual review of biomedical data science · 2026Review
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- Assessing real-world natural history of indolent systemic mastocytosis: A retrospective matched cohort study.The journal of allergy and clinical immunology. Global · 2026Article
- Weight-loss dynamics with tirzepatide versus semaglutide.PNAS nexus · 2026Article
- Measuring the gap: correlating synthetic-to-real drift with PHI de-identification performance.Genomics & informatics · 2026Article
- Challenges in Evaluating Clinical Remission in Severe Asthma: Insights from the Mayo Clinic.Advances in therapy · 2026Article
- Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models.BMC medical informatics and decision making · 2026Article
- Auditable large language model curation of clinical notes refines glucagon-like peptide-1 initiation, persistence ascertainment, and compounding use beyond prescription records.Biology methods & protocols · 2026Article
- GLP-1-based incretin therapy is associated with lower incident Alzheimer's disease and cardiorenal events in adults with documented neuropsychiatric, cognitive, or sensory risk.Biology methods & protocols · 2026Article
- Decoding the hallmarks of GLP-1RA weight-loss super-responders.Biology methods & protocols · 2026Article
- Weight trajectories after last tirzepatide or semaglutide prescription across a federated health network.Biology methods & protocols · 2026Article
- Semaglutide use linked to lower COVID-19 and influenza severity with better renal outcomes after pandemic or seasonal infection.Biology methods & protocols · 2026Article
- Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform.npj health systems · 2026Article
- HER2-Ultralow: Prevalence, Characteristics, and Treatment Choices Among Advanced Breast Cancer Patients With Tumors Initially Scored as IHC 0.The breast journal · 2026Article
- A Markov Chain Replacement Strategy for Surrogate Identifiers: Minimizing Re-Identification Risk While Preserving Text Reuse.Electronics · 2025Article
- Impact of empirical treatment failure on health care resource utilization and costs among female patients with uncomplicated urinary tract infections in a US-based Integrated Health Delivery Network.Journal of managed care & specialty pharmacy · 2025Article
- External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model.Mayo Clinic proceedings. Digital health · 2025Article
- Article
- What is the patient re-identification risk from using de-identified clinical free text data for health research?AI and ethics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of personally identifiable information (PII) in natural language portions of electronic health records (EHRs) constrains their broad reuse. Despite continuous improvements in automated detection of PII, residual identifiers require manual validation and correction. Here, we describe an automated de-identification system that employs an ensemble architecture, incorporating attention-based deep-learning models and rule-based methods, supported by heuristics for detecting PII in EHR data. Detected identifiers are then transformed into plausible, though fictional, surrogates to further obfuscate any leaked identifier. Our approach outperforms existing tools, with a recall of 0.992 and precision of 0.979 on the i2b2 2014 dataset and a recall of 0.994 and precision of 0.967 on a dataset of 10,000 notes from the Mayo Clinic. The de-identification system presented here enables the generation of de-identified patient data at the scale required for modern machine-learning applications to help accelerate medical discoveries.
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.