JMIR Publications

ABSTRACT

Background:

Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to interoperability, epidemiological research, data sharing, and the application of computational methods, including artificial intelligence. These barriers complicate collaboration across regions and organizations and hinder technological progress in public health.

Objective:

To propose and release the first open, machine-readable format for representing case and syndrome definitions, together with tools and resources that enable their standardized and scalable use.

Methods:

We developed the Open Syndrome Definition (OSD), a structured, machine-readable schema for representing case and syndrome definitions. We compiled official public health case definitions from multiple institutions and converted them into standardized, machine-readable representations using open-source tools. These tools, available through GitHub under the MIT license, automate the translation of narrative definitions into structured data. We also created a platform for browsing, analyzing, and contributing new definitions at our initiative website.

Results:

The OSD format enabled consistent, automated representation of case definitions across different diseases and jurisdictions. The conversion tools achieved high semantic fidelity between narrative and structured representations, supporting human verification and automated analysis. The dataset and accompanying tools demonstrated interoperability across diverse sources and use cases.

Conclusions:

The Open Syndrome Definition establishes a foundation for consistent and machine-readable public health definitions, facilitating reproducible research and interoperability at scale. By enabling systematic data exchange and AI-driven analysis, it strengthens public health preparedness and supports more rapid, coordinated responses to emerging health threats.