The Open Syndrome Definition as a Machine-Readable Standard for Public Health: Design and Implementation Study

Area of Science:

• Public Health Informatics
• Computational Epidemiology
• Health Data Standards

Background:

• Standardized, machine-readable case definitions are crucial for public health.
• Current formats hinder interoperability, research, data sharing, and AI applications.
• Barriers complicate collaboration and slow technological advancements in public health.

Purpose of the Study:

• To propose and release the first open, machine-readable format for case and syndrome definitions.
• To provide tools and resources for standardized and scalable use of these definitions.
• To improve public health communication and response capabilities.

Main Methods:

• Developed the Open Syndrome Definition, a structured, machine-readable schema.
• Compiled and converted official case definitions into standardized, machine-readable representations using open-source tools.
• Created a platform for browsing, analyzing, and contributing definitions, with tools available on GitHub.

Main Results:

• The Open Syndrome Definition format ensures consistent, automated representation of case definitions.
• Conversion tools demonstrated high semantic fidelity between narrative and structured definitions.
• Achieved structural and semantic interoperability, integrating medical ontologies and demonstrating practical applicability with a data filtering prototype.

Conclusions:

• The Open Syndrome Definition provides a foundation for consistent, machine-readable public health data.
• Facilitates reproducible research, interoperability, and AI-driven analysis at scale.
• Strengthens public health preparedness and enables faster, coordinated responses to health threats.