可解释的基于变压器的建模用于以病原体为导向的食品安全检查等级预测,使用纽约州开放数据
Omer Faruk Sari1, Mohamed Bader-El-Den1,2, Volkan Ince1
1School of Computing, University of Portsmouth, Lion Terrace, Portsmouth PO1 3HE, UK.
Foods (Basel, Switzerland)
|January 28, 2026
概括
这项研究开发了一个AI框架,使用检查数据预测食品安全检查等级. 像RoBERTa这样的变压器模型实现了高精度,识别了关键的食品安全风险,以改善公共卫生监测.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 食品安全 食品安全
背景情况:
- 食物传播的病原体对公共健康构成重大风险.
- 早期识别不安全食品的情况对于预防至关重要.
- 常规检查为风险评估产生了有价值的数据.
研究的目的:
- 开发一个可解释的基于变压器的框架,用于预测食品安全检查等级.
- 利用多式联运检查数据,结合结构化元数据和非结构化缺陷叙述.
- 评估各种机器学习和深度学习模型的性能,包括变压器.
主要方法:
- 结合结构化元数据与非结构化的缺陷叙述.
- 评估了经典机器学习 (LightGBM),深度学习 (BiLSTM) 和变压器模型 (RoBERTa).
- 为了模型的可解释性,使用了夏普利添加式解释 (SHAP).
主要成果:
- 罗伯塔以0.96.6的F1得分获得了最高的表现.
- BiLSTM和LightGBM也表现出强的表现 (F1=0.95和F1=0.92,分别).
- SHAP分析确定了病原体相关危险的关键指标,包括温度滥用,害虫和不卫生做法.
结论:
- 具有可解释AI (XAI) 的基于变压器的模型可以有效地支持面向病原体的监测和实时风险评估.
- 多模式人工智能方法可以提高检查效率,加强公共卫生监督.
- 开发的框架显示了改善食品安全和减少食源性疾病的潜力.
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