Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
This study advances the utility of synthetic study data in hematology, particularly for Acute Myeloid Leukemia (AML), by facilitating its integration into healthcare systems and research platforms through standardization into the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR) formats. In our previous work, we addressed the need for high-quality patient data and used CTAB-GAN+ and Normalizing Flow (NFlow) to synthesize data from 1606 patients across four multicenter AML clinical trials. We published the generated synthetic cohorts, that accurately replicate the distributions of key demographic, laboratory, molecular, and cytogenetic variables, alongside patient outcomes, demonstrating high fidelity and usability. The conversion to the OMOP format opens avenues for comparative observational multi-center research by enabling seamless combination with related OMOP datasets, thereby broadening the scope of AML research. Similarly, standardization into FHIR facilitates further developments of applications, e.g. via the SMART-on-FHIR platform, offering realistic test data. This effort aims to foster a more collaborative research environment and facilitate the development of innovative tools and applications in AML care and research.
Details
Original language | English |
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Pages (from-to) | 1472-1476 |
Number of pages | 5 |
Journal | Studies in health technology and informatics |
Volume | 316 |
Publication status | Published - 22 Aug 2024 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0002-1887-4772/work/166764624 |
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ORCID | /0000-0002-9888-8460/work/166764819 |
Scopus | 85202002537 |
Keywords
Research priority areas of TU Dresden
DFG Classification of Subject Areas according to Review Boards
Keywords
- Electronic Health Records, Health Information Interoperability, Hematology, Humans, Leukemia, Myeloid, Acute, Outcome Assessment, Health Care