heartland-synthetic: A Reproducible Generator and Benchmark Dataset for Rural Heart-Failure Research Workflows
Year: 2026
Type: article
Abstract: Background: Research on rural heart-failure implementation often requires variables absent from general-purpose synthetic-patient generators, including distance to cardiology and structured social-support measures. Real clinical data may be unavailable during early protocol and interoperability development. Objective: To describe heartland-synthetic v0.2.2 and validate the integrity and reproducibility of its public 1,000-row benchmark cohort. Methods: The Python package generates tabular cohorts, HEARTLAND risk scores, longitudinal monitoring series, and REDCap/FHIR R4 exports from literature-informed distributions. Tests evaluated reproducibility, scoring, distributions, rural effects, outcomes, and exports. The public seed-42 benchmark was independently checked for shape, completeness, uniqueness, checksum, and descriptive distributions. Results: All 65 automated tests passed. The benchmark contained 1,000 unique rows and 31 columns, with no missing cells or duplicate patient identifiers. Conclusions: The package supports reproducible software testing, methods development, and training without real patient data. Its modeled distributions and outcomes are not external validation, population estimates, causal evidence, or clinical predictions.
Author: Muller Ferreira, Vicky
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7204580937
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