ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2010
Anonymization of administrative billing codes with repeated diagnoses through censoring.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- The Anonymous Data Warehouse: A Hands-On Framework for Anonymizing Data From Digital Health Applications.Cureus · 2024Article
- Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.Journal of medical Internet research · 2019Article
- Genetic data sharing and privacy.Neuroinformatics · 2015Article
- The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.Genetics in medicine : official journal of the American College of Medical Genetics · 2013Review
- Reducing patient re-identification risk for laboratory results within research datasets.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Anonymization of longitudinal electronic medical records.IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society · 2012Article
- De-identification methods for open health data: the case of the Heritage Health Prize claims dataset.Journal of medical Internet research · 2012Article
- Attribute Utility Motivated k-anonymization of datasets to support the heterogeneous needs of biomedical researchers.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2011Article
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
Patient-specific data from electronic medical records (EMRs) is increasingly shared in a de-identified form to support research. However, EMRs are susceptible to noise, error, and variation, which can limit their utility for reuse. One way to enhance the utility of EMRs is to record the number of times diagnosis codes are assigned to a patient when this data is shared. This is, however, challenging because releasing such data may be leveraged to compromise patients' identity. In this paper, we present an approach that, to the best of our knowledge, is the first that can prevent re-identification through repeated diagnosis codes. Our method transforms records to preserve privacy while retaining much of their utility. Experiments conducted using 2676 patients from the EMR system of the Vanderbilt University Medical Center verify that our method is able to retain an average of 95.4% of the diagnosis codes in a common data sharing scenario.
Indexed as
Identifiers
21347085PMC3041421What 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.