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一个基于机器学习的框架,用于识别来自社交媒体数据的消费者产品损伤
Harmya Bhatt1, Souvik Das2, Yi Jade Han3
1Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Injury
|December 9, 2025
概括
本研究引入了一种机器学习框架,使用社交媒体数据快速检测与产品相关的伤害,改善消费者安全监督,并使更快的干预成为可能.
科学领域:
- 消费者产品安全 产品安全
- 公共卫生监督 公共卫生监督
- 机器学习应用 机器学习应用
背景情况:
- 每年都会发生数以百万计的与产品相关的伤害,传统的监控方法导致识别伤害模式的延迟.
- 目前依赖医院数据的方法很慢,阻碍了如产品召回等及时的预防行动.
- 这种延迟导致了与消费者产品相关的持续伤害.
研究的目的:
- 开发和评估用于实时伤害监测的机器学习 (ML) 框架.
- 从社交媒体中提取产品损害细节,以便快速识别趋势并进行干预.
- 提高消费者产品安全监测的速度和有效性.
主要方法:
- 提出了一个两阶段的ML框架,利用社交媒体帖子 (Reddit) 和国家电子伤害监控系统 (NEISS) 数据.
- 第1阶段将职位分类为受伤相关的职位或不使用在不同数据集上训练的ML模型.
- 第二阶段使用ML模型预测受伤的身体部位和受伤诊断代码.
主要成果:
- 第1阶段的模型 (LSTM,GRU) 在分类受伤相关职位方面获得了72%的F1得分.
- 第二阶段模型 (SGD) 显示,预测受伤的身体部位的F1得分为86%.
- 第二阶段的模型还在伤害诊断代码预测方面获得了76%的F1分数.
结论:
- ML框架显示了伤害监测的有希望的准确性.
- 通过这种框架进行社交媒体数据分析,可以识别新出现的与产品相关的伤害趋势.
- 拟议的系统可以显著增强消费产品的现有公共卫生监测工作.
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