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使用风险评估矩阵对食品事件的风险分类,用于人工智能支持的风险识别

Sina Röhrs¹, Sascha Rohn², Yvonne Pfeifer¹

¹SGS Germany GmbH, Health and Nutrition, Heidenkampsweg 99, 20097 Hamburg, Germany.

Foods (Basel, Switzerland)

|November 27, 2024

概括

这项研究引入了人工智能 (AI) 的多标准决策分析 (MCDA) 方法,用于对食品事故报告进行分类,从而能够早期预防性识别食物传播疾病风险.

科学领域:

食品安全科学食品安全科学
数据科学和人工智能数据科学和人工智能
公共卫生风险评估公共卫生风险评估

背景情况:

食物传播疾病是一个重大的全球健康挑战,世界卫生组织估计患病率很高.
数字化提供了大量的数据资源,但对食品安全的有效利用仍然是一个挑战.
现有的方法可能无法及早检测新出现的食源性风险.

研究的目的:

开发基于多标准决策分析 (MCDA) 的评估方案,用于对食品事故报告进行自主风险分类.
将该计划整合到人工智能 (AI) 支持的数据库中,用于主动的食品安全管理.
为了能够更早地识别新的或新出现的食品安全风险,采取预防性行动.

主要方法:

为食品事故报告量身定制的基于MCDA的评估方案的开发.
在AI支持的数据库框架内实施该计划.
通过手动运行进行测试和验证,以评估可行性和效率.

主要成果:

基于MCDA的方案使得事件报告的有效,节省时间的初步风险分类成为可能.
该方法有助于快速识别相关报告进行进一步审查.
人工智能数据和评估标准的增加大大提高了风险评估的准确性.

结论:

开发的MCDA方案适用于AI支持的数据库,允许对食品事故报告进行自主评估.
这种预防方法有助于早期识别风险,可能在正式通知之前.
未来的工作应该集中在优化数据的利用和处理,以提高食品安全的AI系统.

关键词:

人工智能的人工智能是人工智能.早期预警的早期警告.食品安全食品安全风险评估风险评估风险评估

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