基于多标签分类的COVID-19疫苗不良事件检测与各种标签选择策略的多标签分类
IEEE journal of biomedical and health informatics
|July 7, 2023
概括
这项研究引入了一种新的多标签分类方法,用于在报告中准确检测疫苗不良事件 (VAE). 基于主题的策略显著提高了VAE检测的准确性和可解释性,改善了疫苗安全监测.
科学领域:
- 药物监测和医疗信息学
- 计算语言学和机器学习
背景情况:
- 分析疫苗不良事件 (VAE) 报告需要仔细考虑医疗背景,以避免误解.
- 准确有效地检测VAE对于不断改进疫苗安全监测至关重要.
研究的目的:
- 提出和评估一种多标签分类方法,使用基于术语和主题的标签选择策略来增强VAE检测.
- 提高从大型数据集中识别潜在疫苗不良事件的准确性和效率.
主要方法:
- 采用主题建模来生成基于规则的标签依赖,从VAE报告中的"监管活动医学词典"术语.
- 应用多个标签选择策略,包括一个对其余 (OvsR),问题转换 (PT) 和算法适应 (AA) 进行多标签分类.
- 将拟议方法的性能与最先进的深度学习模型 (LSTM,BERT) 进行了比较.
主要成果:
- 基于主题的PT方法在COVID-19 VAE数据集上提高了高达33.69%的准确性,提高了模型的稳定性和可解释性.
- 基于主题的OvsR方法实现了98.88%的最佳准确率,而基于主题的标签的AA方法提高了准确率高达87.36%.
- 深度学习方法 (LSTM,BERT) 的性能相对较低,准确率分别为71.89%和64.63%.
结论:
- 拟议的多标签分类方法,结合领域知识和各种标签选择策略,显著提高了VAE检测准确性.
- 该研究证明了基于主题的策略在提高VAE检测模型的准确性和可解释性方面的有效性.
- 研究结果表明,通过先进的计算方法,可以改善疫苗安全监测系统的强大框架.
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