预测禁止物质:利用GNN和可解释的AI用于体育反兴奋剂
Alina Gavrish1, Yang Yang1, Julie Loesch1
1Department of Advanced Computing Sciences, Maastricht University, Maastricht, the Netherlands.
Studies in health technology and informatics
|May 17, 2025
概括
本研究使用图形神经网络 (GNN) 和可解释AI (XAI) 来识别禁止的兴奋剂物质. 这种新的方法通过基于分子数据准确地分类物质来加强反兴奋剂工作.
科学领域:
- 运动科学 运动科学 运动科学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 运动中的公平竞争依赖于检测禁止物质.
- 当前的方法面临着新的和新兴的兴奋剂的挑战.
- 对监管机构来说,准确识别禁止物质至关重要.
研究的目的:
- 引入一种基于人工智能的新方法来将物质分类为禁止或非禁止物质.
- 为了利用图形神经网络 (GNN) 和可解释AI (XAI) 进行兴奋剂检测.
- 加强兴奋剂控制机制的透明度和问责制.
主要方法:
- 利用异质知识图 (KG) 来构建预测模型.
- 使用图形神经网络 (GNN) 来进行物质分类.
- 应用可解释AI (XAI) 技术,包括集成梯度和突出度,用于模型可解释性.
主要成果:
- 开发了一种能够根据分子和药理学数据对物质进行分类的预测模型.
- 证明了GNN在识别禁止物质方面的有效性.
- 通过XAI方法提供透明和可追溯的分类决策.
结论:
- 由人工智能驱动的方法为兴奋剂检测提供了一种强大而新的方法.
- 可解释的人工智能增强了对反兴奋剂分类的信任和问责制.
- 这项工作支持监管机构做出明智的决策,以确保体育的诚信.
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