ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
Comparative Study of Various Approaches for Ensemble-based De-identification of Electronic Health Record Narratives.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 3 citations in OpenAlex.
- Not Fully Synthetic: LLM-based Hybrid Approaches Towards Privacy-Preserving Clinical Note Sharing.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
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 at 1 institution in 1 country.
Funding
Abstract
De-identification of electric health record narratives is a fundamental task applying natural language processing to better protect patient information privacy. We explore different types of ensemble learning methods to improve clinical text de-identification. We present two ensemble-based approaches for combining multiple predictive models. The first method selects an optimal subset of de-identification models by greedy exclusion. This ensemble pruning allows one to save computational time or physical resources while achieving similar or better performance than the ensemble of all members. The second method uses a sequence of words to train a sequential model. For this sequence labelling-based stacked ensemble, we employ search-based structured prediction and bidirectional long short-term memory algorithms. We create ensembles consisting of de-identification models trained on two clinical text corpora. Experimental results show that our ensemble systems can effectively integrate predictions from individual models and offer better generalization across two different corpora.
Indexed as
Identifiers
33936439PMC8075417W3158708644What 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.