12/31/2023 0 Comments Vault 101 medical data system![]() Preservation of biological relationships. The following four additional key requirements are envisioned to be critical for making synthetic data usable: In this article, we propose a framework to focus on synthetic data generation ensuring data utility, that is, clinically meaningful, and the preservation of patient privacy. Synthetic data could be generated to be intentionally distinct from real data to reveal biases in algorithmic performance.ĭespite all the advantages outlined above, there is no synthetic data generation and evaluation approach that can be applied to healthcare data to ensure that the generated data preserves key ground truth characteristics (such as sensible biological relationships between variables) while ensuring privacy. (6) Benchmarking and validation capabilities-This is useful when comparing different machine learning methods against a standardized dataset while focusing on a specific set of diseases (e.g., cardiovascular diseases). ![]() discovered that anonymization measures for real data may compromise data utility due to information loss. argue that synthetic data can supplement real data by either filling gaps or enlarging a subgroup dataset. (5) Completeness-It can be difficult to conduct unbiased data research if there are inherent biases in the data. (4) Patient privacy protection-The social-demographic and health-related content in the healthcare data makes patient identification more likely and therefore a fully synthetic approach can better mitigate this risk according to Park and Ghosh. For example, one typical scenario could be to test the scalability and robustness of an algorithm. (3) Test efficiency-Lee and Whalen's work identified that using a synthetic data generation model can efficiently improve algorithms or functions in an information system 4 by generating desired data on-the-fly. (2) Cost-efficiency-In the context of healthcare data collection, using a synthetic data generation model for benchmarking and validation is significantly more cost-efficiency than expanding the population coverage of real-world data due to the cost of scaling-up collection and processing pipelines. The ability to streamline data access approvals with synthetic datasets could increase the speed of research innovation. 3 Moreover, the legal bases for sharing of these routinely collected data may present restrictions on use that need to be monitored by data controllers. (1) Ease of access-Access to individual record level real-world data, even in pseudonymized format with strong personal identifiers removed, tends to be strictly regulated to control the risk of inadvertent patient reidentification according to Sebastian et al.'s work. 2 However, there is a need for developing a synthetic dataset that would complement such rich real-world data for various reasons outlined below. proposed an application for diagnosis and prediction of diseases. For example, in Khalid et al.'s work, the data are used to yield new insights into drug use patterns 1 and Ravizza et al. The recent development of intelligent applications makes these data attractive for the application of various data mining and machine learning algorithms. We include findings and new insights from synthetic datasets modeled on both the Indian liver patient dataset and UK primary care dataset to demonstrate the application of this framework under different scenarios.Įlectronic healthcare record (EHR) data are a rich source of clinical symptoms, diagnoses, investigations, and treatments. To our knowledge, this is the first article to propose a framework to generate and evaluate synthetic healthcare data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data while also ensuring privacy. This article discusses the key requirements of synthetic data for multiple purposes and proposes an approach to generate and evaluate synthetic data focused on, but not limited to, cross-sectional healthcare data. Synthetic data could potentially be an alternative to real-world data for these purposes as well as reveal any biases in the data used for algorithm development. There has been increasing interest in utilizing these data for new purposes such as for machine learning to develop predictive algorithms to aid diagnostic and treatment decisions. Electronic healthcare record data have been used to study risk factors of disease, treatment effectiveness and safety, and to inform healthcare service planning.
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