ArticleJCO clinical cancer informatics2024
Actionability of Synthetic Data in a Heterogeneous and Rare Health Care Demographic: Adolescents and Young Adults With Cancer.
Article in JCO clinical cancer informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Investigating the utilization of LIME-enhanced AI and generated data for evaluating the risks associated with radial artery puncture procedures.Scientific reports · 2026Article
- Knowledge Representation of a Multicenter Adolescent and Young Adult Cancer Infrastructure: Development of the STRONG AYA Knowledge Graph.JCO clinical cancer informatics · 2026Article
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6 authors.
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Abstract
purposeResearch on rare diseases and atypical health care demographics is often slowed by high interparticipant heterogeneity and overall scarcity of data. Synthetic data (SD) have been proposed as means for data sharing, enlargement, and diversification, by artificially generating real phenomena while obscuring the real patient data. The utility of SD is actively scrutinized in health care research, but the role of sample size for actionability of SD is insufficiently explored. We aim to understand the interplay of actionability and sample size by generating SD sets of varying sizes from gradually diminishing amounts of real individuals' data. We evaluate the actionability of SD in a highly heterogeneous and rare demographic: adolescents and young adults (AYAs) with cancer.
methodsA population-based cross-sectional cohort study of 3,735 AYAs was subsampled at random to produce 13 training data sets of varying sample sizes. We studied four distinct generator architectures built on the open-source Synthetic Data Vault library. Each architecture was used to generate SD of varying sizes on the basis of each aforementioned training subsets. SD actionability was assessed by comparing the resulting SD with their respective real data against three metrics-veracity, utility, and privacy concealment.
resultsAll examined generator architectures yielded actionable data when generating SD with sizes similar to the real data. Large SD sample size increased veracity but generally increased privacy risks. Using fewer training participants led to faster convergence in veracity, but partially exacerbated privacy concealment issues.
conclusionSD is a potentially promising option for data sharing and data augmentation, yet sample size plays a significant role in its actionability. SD generation should go hand-in-hand with consistent scrutiny, and sample size should be carefully considered in this process.
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