ArticleIEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society2012
Anonymization of longitudinal electronic medical records.
Article in IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithms to anonymize structured medical and healthcare data: A systematic review.Frontiers in bioinformatics · 2022Pooled it
- PanDa Game: Optimized Privacy-Preserving Publishing of Individual-Level Pandemic Data Based on a Game Theoretic Model.IEEE transactions on nanobioscience · 2023Article
- What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions.JMIR formative research · 2020Review
- The Impact of Medical Big Data Anonymization on Early Acute Kidney Injury Risk Prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.Journal of medical Internet research · 2019Article
- Privacy Policy and Technology in Biomedical Data Science.Annual review of biomedical data science · 2018Article
- Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress.Yearbook of medical informatics · 2017Review
- Secondary use of clinical data: the Vanderbilt approach.Journal of biomedical informatics · 2014Article
- Reducing patient re-identification risk for laboratory results within research datasets.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Getting personal: understanding how genetic variation affects clinical outcomes in patients with tetralogy of Fallot.Pediatric research · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Electronic medical record (EMR) systems have enabled healthcare providers to collect detailed patient information from the primary care domain. At the same time, longitudinal data from EMRs are increasingly combined with biorepositories to generate personalized clinical decision support protocols. Emerging policies encourage investigators to disseminate such data in a deidentified form for reuse and collaboration, but organizations are hesitant to do so because they fear such actions will jeopardize patient privacy. In particular, there are concerns that residual demographic and clinical features could be exploited for reidentification purposes. Various approaches have been developed to anonymize clinical data, but they neglect temporal information and are, thus, insufficient for emerging biomedical research paradigms. This paper proposes a novel approach to share patient-specific longitudinal data that offers robust privacy guarantees, while preserving data utility for many biomedical investigations. Our approach aggregates temporal and diagnostic information using heuristics inspired from sequence alignment and clustering methods. We demonstrate that the proposed approach can generate anonymized data that permit effective biomedical analysis using several patient cohorts derived from the EMR system of the Vanderbilt University Medical Center.
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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.