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Domain Knowledge-Driven Generation of Synthetic Healthcare Data.

Atiye Sadat Hashemi1, Amira Soliman1, Jens Lundström1

  • 1Center for Applied Intelligent Systems Research in Health, Halmstad University, Sweden.

Studies in Health Technology and Informatics
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for generating synthetic electronic health records (EHRs) by integrating domain knowledge. This approach addresses challenges with real patient data, ensuring privacy and clinical validity for AI applications.

Keywords:
Domain KnowledgeEHRRepresentation LearningSynthetic Data

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Health Informatics
  • Medical Data Science

Background:

  • Longitudinal patient data offers significant potential for healthcare transformation via artificial intelligence (AI).
  • Accessing real-world healthcare data is hindered by ethical and legal constraints.
  • Electronic Health Records (EHRs) present challenges such as bias, heterogeneity, imbalanced data, and small sample sizes.

Purpose of the Study:

  • To introduce a domain knowledge-driven framework for generating synthetic EHRs.
  • To provide an alternative to methods relying solely on EHR data or expert knowledge.
  • To maintain data utility, fidelity, and clinical validity while ensuring patient privacy.

Main Methods:

  • Development of a framework that leverages external medical knowledge sources.
  • Integration of domain knowledge into the training algorithm for synthetic EHR generation.
  • Focus on preserving data utility, fidelity, and clinical validity.

Main Results:

  • The proposed framework successfully generates synthetic EHRs.
  • The method addresses common challenges associated with real EHR data.
  • Patient privacy is preserved through the synthetic data generation process.

Conclusions:

  • A domain knowledge-driven framework offers a viable solution for generating high-quality synthetic EHRs.
  • This approach mitigates issues related to real-world data access and EHR data quality.
  • The framework facilitates the use of AI in healthcare by providing privacy-preserving, clinically valid synthetic data.