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A Novel Taxonomy for Navigating and Classifying Synthetic Data in Healthcare Applications.

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Synthetic data offers a solution to healthcare data sharing challenges. This paper introduces a taxonomy to navigate synthetic data types, modalities, and transformations for improved healthcare research.

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Area of Science:

  • Health Informatics
  • Data Science
  • Biomedical Data

Background:

  • Data-driven technologies enhance healthcare efficiency and reliability.
  • Privacy constraints limit healthcare data sharing, creating a demand for alternative solutions.
  • Synthetic data is emerging as a promising approach to address these data challenges.

Purpose of the Study:

  • To propose a novel taxonomy for synthetic data in healthcare.
  • To categorize synthetic data based on Data Proportion, Data Modality, and Data Transformation.
  • To provide a framework for researchers to understand the potential and challenges of synthetic data in healthcare.

Main Methods:

  • Development of a new taxonomy for synthetic data in healthcare.
  • Categorization into three main varieties: Data Proportion, Data Modality, and Data Transformation.
  • Analysis of pros, cons, and challenges associated with each variety.

Main Results:

  • The proposed taxonomy classifies synthetic data by its proportion in a dataset, its data format (modality), and its application in data transformation.
  • Each category (Data Proportion, Data Modality, Data Transformation) presents unique advantages and challenges.
  • The taxonomy highlights the interdependencies and potential overlaps between different synthetic data varieties.

Conclusions:

  • The taxonomy provides a structured overview of synthetic data in healthcare, aiding researchers in its application.
  • It clarifies the possibilities and limitations of using synthetic data for various healthcare data types and research goals.
  • This framework facilitates a better understanding of synthetic data's role in overcoming healthcare data sharing and privacy issues.