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An open-source educational dataset from UNSW Health Big Data Research Center that generates 50,000 simulated patient records using causal Directed Acyclic Graphs (DAGs). It offers two versions: a clean cohort and real EMR-style "dirty data", supporting medical informatics teaching in areas like causal inference, survival analysis, and data cleaning.
Section 01
PRIME-CVD is an open-source educational dataset developed by UNSW Health Big Data Research Center (CBDRH). It generates 50,000 simulated patient records via causal Directed Acyclic Graph (DAG), offering two versions: clean analysis-ready queue and real EMR-style "dirty" data. It supports teaching of causal inference, survival analysis, data cleaning, etc., while ensuring full privacy protection.
Section 02
Medical informatics education faces a long-standing conflict between data access and privacy. Real EMR data is sensitive and regulated, hard to share; fully synthetic data lacks real-world complexity. PRIME-CVD is designed to solve this by providing privacy-safe, realistic simulated data.
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| Feature | Description |
|---|---|
| Privacy Safety | Fully synthetic data, no privacy leakage risk |
| Education-Oriented | Clear DAG and EMR artifact design |
| Reproducible | Deterministic process for dataset reconstruction |
| Dual Assets | Clean queue + dirty EMR covering full analysis flow |
| Open Access | Code, data, tutorials all open-source |
Section 07
PRIME-CVD innovates medical informatics education by balancing privacy and realism. Developed by UNSW CBDRH team (Dr. Nic Kuo, Dr. Marzia Hoque, Prof. Blanca Gallego, Prof. Louisa Jorm). Data available via figshare: Clean queue (https://doi.org/10.6084/m9.figshare.31395765.v1), EMR-style data (https://doi.org/10.6084/m9.figshare.31403028.v1).
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