ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Data Synthesis Reinvented: Preserving Missing Patterns for Enhanced Analysis.IEEE transactions on knowledge and data engineering · 2025Article
- Exploring the use of Artificial Genomes for Genome-wide Association Studies through the lens of Utility and Privacy.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Preserving Missing Data Distribution in Synthetic Data.Proceedings of the ... International World-Wide Web Conference. International WWW ConferenceArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Big data coupled with precision medicine has the potential to significantly improve our understanding and treatment of complex disorders, such as cancer, diabetes, depression, etc. However, the essential problem is that data are stuck in silos, and it is difficult to precisely identify which data would be relevant and useful for any particular type of analysis. While the process to acquire and access biomedical data requires significant effort, in many cases the data may not provide much insight to the problem at hand. Therefore, there is a need to be able to measure the utility/relevance of additional datasets for a particular biomedical research task without direct access to the data. Towards this, in this paper, we develop a privacy-preserving approach to create synthetic data that can provide a firstorder approximation of utility. We evaluate the proposed approach with several biomedical datasets in the context of regression and classification tasks and discuss how it can be incorporated into existing data management systems such as REDCap.
Indexed as
Identifiers
29854240PMC5977652What 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.