ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Efficient Active Learning for Electronic Medical Record De-identification.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05277116 (The Electronic Medical Records and GEnomics), which is not on this map. Cited by 7 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.
The Electronic Medical Records and GEnomics (eMERGE) Network Genomic Risk Assessment
Who cites it
7 citing papers in PubMed.
- An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation.JMIR medical informatics · 2022Article
- The OpenDeID corpus for patient de-identification.Scientific reports · 2021Article
- Building a best-in-class automated de-identification tool for electronic health records through ensemble learning.Patterns (New York, N.Y.) · 2021Article
- Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction.Journal of biomedical informatics · 2021Article
- Article
- Article
- Clinical concept annotation with contextual word embedding in active transfer learning environment.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Electronic medical records are often de-identified before disseminated for secondary uses. However, unstructured natural language records are challenging to de-identify while utilizing a considerable amount of expensive human annotation. In this investigation, we incorporate active learning into the de-identification workflow to reduce annotation requirements. We apply this approach to a real clinical trials dataset and a publicly available i2b2 dataset to illustrate that, when the machine learning de-identification system can actively request information to help create a better model from beyond the system (e.g., a knowledgeable human assistant), less training data will be needed to maintain or improve the performance of trained models in comparison to the typical passive learning framework. Specifically, with a batch size of 10 documents, it requires only 40 documents for an active learning approach to reach an F-measure of 0.9, while passive learning needs at least 25% more data for training a comparable model.
Identifiers
31259000PMC6568071What 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.