ArticlePloS one2013
Enabling genomic-phenomic association discovery without sacrificing anonymity.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 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
10 citing papers in PubMed.
- Expected 10-anonymity of HyperLogLog sketches for federated queries of clinical data repositories.Bioinformatics (Oxford, England) · 2021Article
- Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.Journal of medical Internet research · 2019Article
- How Sensitive Is Genetic Data?Biopreservation and biobanking · 2017Article
- A multi-institution evaluation of clinical profile anonymization.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Sharing and Reuse of Sensitive Data and Samples: Supporting Researchers in Identifying Ethical and Legal Requirements.Biopreservation and biobanking · 2015Article
- Phenotype-Driven Plasma Biobanking Strategies and Methods.Journal of personalized medicine · 2015Article
- Size matters: how population size influences genotype-phenotype association studies in anonymized data.Journal of biomedical informatics · 2014Article
- Secondary use of clinical data: the Vanderbilt approach.Journal of biomedical informatics · 2014Article
- Ethical and practical challenges to studying patients who opt out of large-scale biorepository research.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Personalized medicine: challenges and opportunities for translational bioinformatics.Personalized medicine · 2013Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Health information technologies facilitate the collection of massive quantities of patient-level data. A growing body of research demonstrates that such information can support novel, large-scale biomedical investigations at a fraction of the cost of traditional prospective studies. While healthcare organizations are being encouraged to share these data in a de-identified form, there is hesitation over concerns that it will allow corresponding patients to be re-identified. Currently proposed technologies to anonymize clinical data may make unrealistic assumptions with respect to the capabilities of a recipient to ascertain a patients identity. We show that more pragmatic assumptions enable the design of anonymization algorithms that permit the dissemination of detailed clinical profiles with provable guarantees of protection. We demonstrate this strategy with a dataset of over one million medical records and show that 192 genotype-phenotype associations can be discovered with fidelity equivalent to non-anonymized clinical data.
Indexed as
Identifiers
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.