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The Open Syndrome Definition

arXiv 2509.25434 ↗First posted 29 Sep 2025cs.AIcited by 0

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Abstract (arXiv)

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, the exchange of qualitative data, and the effective application of computational analysis methods, including artificial intelligence (AI). This complicates comparisons and collaborations across organizations and regions, limits data integration, and hinders technological innovation in public health. To address these issues, we propose the first open, machine-readable format for representing case and syndrome definitions. Additionally, we introduce the first comprehensive dataset of standardized case definitions and tools to convert existing human-readable definitions into machine-readable formats. We also provide an accessible online platform for browsing, analyzing, and contributing new definitions, available at https://opensyndrome.org. The Open Syndrome Definition format enables consistent, scalable use of case definitions across systems, unlocking AI's potential to strengthen public health preparedness and response. The source code for the format can be found at https://github.com/OpenSyndrome/schema under the MIT license.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 62 references that are in the map. First posted on arXiv 29 Sep 2025.

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References · 9

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
  3. 2023Mistral 7Bcited by 3,164
  4. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  5. 2024Qwen2.5-Coder Technical Reportcited by 1,203
  6. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 493
  7. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  8. 2023Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learningcited by 50
  9. 2021Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Reviewcited by 35

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