The Open Syndrome Definition
- URL: http://arxiv.org/abs/2509.25434v2
- Date: Wed, 22 Oct 2025 14:07:59 GMT
- Title: The Open Syndrome Definition
- Authors: Ana Paula Gomes Ferreira, Aleksandar Anžel, Izabel Oliva Marcilio de Souza, Helen Hughes, Alex J Elliot, Jude Dzevela Kong, Madlen Schranz, Alexander Ullrich, Georges Hattab,
- Abstract summary: We propose the first open, machine-readable format for representing case and syndrome definitions.<n>We introduce the first comprehensive dataset of standardized case definitions and tools to convert existing human-readable definitions into machine-readable formats.<n>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.
- Score: 61.0983330391914
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Identity Card Presentation Attack Detection: A Systematic Review [7.7489419818764596]
Deep Learning has driven advances in Presentation Attack Detection.<n>The field is fundamentally limited by a lack of data and the poor generalisation of models.<n>This review consolidates our findings, identifies critical research gaps, and outlines a prescriptive roadmap for future research.
arXiv Detail & Related papers (2025-11-08T15:55:37Z) - Differential Privacy in Machine Learning: From Symbolic AI to LLMs [49.1574468325115]
Differential privacy provides a formal framework to mitigate privacy risks.<n>It ensures that the inclusion or exclusion of any single data point does not significantly alter the output of an algorithm.
arXiv Detail & Related papers (2025-06-13T11:30:35Z) - A Large-Scale Vision-Language Dataset Derived from Open Scientific Literature to Advance Biomedical Generalist AI [70.06771291117965]
We introduce Biomedica, an open-source dataset derived from the PubMed Central Open Access subset.<n>Biomedica contains over 6 million scientific articles and 24 million image-text pairs.<n>We provide scalable streaming and search APIs through a web server, facilitating seamless integration with AI systems.
arXiv Detail & Related papers (2025-03-26T05:56:46Z) - AI-assisted summary of suicide risk Formulation [0.9224875902060083]
This study describes how we developed advanced Natural Language Processing (NLP) algorithms, a branch of Artificial Intelligence (AI)<n>Formulation, associated with suicide risk assessment, is an individualised process that seeks to understand the idiosyncratic nature and development of an individual's problems.
arXiv Detail & Related papers (2024-11-29T16:40:28Z) - Towards Case-based Interpretability for Medical Federated Learning [0.0]
We explore deep generative models to generate case-based explanations in a medical federated learning setting.
Our proof-of-concept focuses on pleural effusion diagnosis and uses publicly available Chest X-ray data.
arXiv Detail & Related papers (2024-08-24T16:42:12Z) - Confidential and Protected Disease Classifier using Fully Homomorphic Encryption [0.09424565541639365]
Many users seek potential causes on platforms like ChatGPT or Bard before consulting a medical professional for their ailment.
Despite the convenience of such platforms, sharing personal medical data online poses risks, including the presence of malicious platforms.
We propose a novel framework combining FHE and Deep Learning for a secure and private diagnosis system.
arXiv Detail & Related papers (2024-05-05T02:10:00Z) - ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Misuse Cases in the Era of Generative AI and Cloud-based Health Information Ecosystem [0.5530212768657544]
This article proposes a zero-trust-based context-aware framework for managing access to the cloud ecosystem.
The framework has two main scoring schemas to maintain the chain of trust.
The analysis is based on a pre-trained machine learning model to generate the semantic and syntactic scores.
arXiv Detail & Related papers (2023-11-28T22:12:07Z) - EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations.
Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
arXiv Detail & Related papers (2022-08-01T18:47:03Z) - Federated Offline Reinforcement Learning [55.326673977320574]
We propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites.
We design the first federated policy optimization algorithm for offline RL with sample complexity.
We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed.
arXiv Detail & Related papers (2022-06-11T18:03:26Z) - Hierarchical Reinforcement Learning for Automatic Disease Diagnosis [52.111516253474285]
We propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning.
The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.
arXiv Detail & Related papers (2020-04-29T15:02:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.