ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Development and validation of a multimodal machine learning prediction model for heart failure after acute myocardial infarction.Clinical and experimental medicine · 2026Article
- From Disease-Specific Models to Broad Clinical Utility: A Perspective on AI Hybrid Ensemble Frameworks.Advanced genetics (Hoboken, N.J.) · 2026Article
- AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.Nature communications · 2024Article
- 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
- Ensuring a safe(r) harbor: Excising personally identifiable information from structured electronic health record data.Journal of clinical and translational science · 2022Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Integrating Eye Tracking and Speech Recognition Accurately Annotates MR Brain Images for Deep Learning: Proof of Principle.Radiology. Artificial intelligence · 2021Article
- Crosslingual named entity recognition for clinical de-identification applied to a COVID-19 Italian data set.Applied soft computing · 2020Article
- Comparative Study of Various Approaches for Ensemble-based De-identification of Electronic Health Record Narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers.Studies in health technology and informatics · 2019Article
- De-identification of Clinical Text via Bi-LSTM-CRF with Neural Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Text de-identification is an application of clinical natural language processing that offers significant efficiency and scalability advantages. Hence, various learning algorithms have been applied to this task to yield better performance. Instead of choosing the best individual learning algorithm, we aim to improve de-identification by constructing ensembles that lead to more accurate classification. We present three different ensemble methods that combine multiple de-identification models trained from deep learning, shallow learning, and rule-based approaches. Each model is capable of automated de-identification without manual medical expertise. Our experimental results show that the stacked learning ensemble is more effective than other ensemble methods, producing the highest recall, the most important metric for de-identification. The stacked ensemble achieved state-of-the-art performance on the 2014 i2b2 dataset with 97.04% precision, 94.45% recall, and 95.73% F
Indexed as
Identifiers
30815108PMC6371277What 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.