DOI: 10.6084/m9.figshare.31403028.v1
title: PRIME-CVD Data Asset 2: Relational EMR-Style Cardiovascular Dataset for Medical Informatics Education

description: OverviewPRIME-CVD Data Asset 2 is an EMR-style relational dataset derived deterministically from PRIME-CVD Data Asset 1 [1]. It represents a cohort of 50,000 simulated adults undergoing primary prevention for cardiovascular disease (CVD), but restructures the clean cohort into a multi-table patient database designed to mimic the structural and lexical messiness of real general-practice EMR systems.Rather than providing a single tidy modelling table, Data Asset 2 forces realistic workflows: linkage, harmonisation, unit handling, and cohort reconstruction — exactly the stuff learners need before they touch governed clinical datasets.This asset supports teaching and assessment in health data engineering, medical informatics, and applied epidemiology, without privacy or governance barriers.What’s in the ZIPThe zip contains three linked CSV tables (linked via <code>Patient_ID</code>):<b>PatientMasterSummary.csv</b><br>One row per patient: demographics, socioeconomic status (IRSD), smoking status (with injected missingness), and coarsened CVD outcome timing.<b>PatientChronicDiseases.csv</b><br>One-to-many diagnosis records: diabetes, CKD, AF represented using heterogeneous free-text and code-like labels.<b>PatientMeasAndPath.csv</b><br>Long-form measurements (<i>e.g.,</i> BMI / SBP / eGFR / HbA1c) with variable naming inconsistency and mixed units.Note: All three tables include an <code>Unnamed: 0</code> column (a benign saved index). Users can safely drop it.SecurityAll individuals are simulated entirely <i>de novo</i> using a fully parameterised DAG configured from publicly available epidemiologic summaries (<i>e.g.,</i> Australian Institute of Health and Welfare, Australian Bureau of Statistics, and peer-reviewed literature). No patient-level electronic medical record (EMR) data were used in model construction, and no machine learning generative models (<i>e.g.,</i> GANs, diffusion models, or large language models) were trained on real clinical data.Because the simulation is entirely mechanism-based rather than data-trained, there is no membership inference risk, no residual linkage risk, and no possibility of re-identification. None of the synthetic individuals correspond to real-world patients, and the dataset contains no direct identifiers, quasi-identifiers, or protected health information.Educational FocusData Asset 2 is designed to teach real-world EMR analytics, including:Relational data linkage using patient identifiersCohort reconstruction from multi-table EMR structureClinical label harmonisationHandling missingnessUnit standardisationFeature engineering from long-form measurement tablesRobust subgroup / equity-aware analysis by IRSDEnd-to-end pipelines that resemble real primary-care workflowsThis lets learners develop competence in realistic EMR workflows before transitioning to governed clinical data.<b>Reference</b><br>[A] Kuo NI-H (2026). PRIME-CVD Data Asset 1: DAG-Simulated Cardiovascular Risk Cohort for Medical Informatics Education. figshare. Dataset. https://doi.org/10.6084/m9.figshare.31395765.v1