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评估四种用于信号检测的机器学习模型
Daniel G Dauner1, Eleazar Leal2, Terrence J Adam3
1Department of Pharmaceutical Care and Health Systems, College of Pharmacy, University of Minnesota Duluth, 232 Life Science, 1110 Kirby Drive, Duluth, MN 55812, USA.
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概括
后勤回归和机器学习模型在检测药物不良事件方面表现相似. 整合案例报告细节可以提高信号检测准确度,超出传统方法.
科学领域:
- 药物监督和药物安全研究.
- 机器学习在医疗保健数据分析中的应用.
背景情况:
- 传统的药监不成比例性分析在处理混因素方面存在局限性.
- 逻辑回归比不成比例分析具有优势,因为它考虑了混因素.
- 机器学习算法为进一步增强药物安全信号检测提供了机会.
研究的目的:
- 为了比较物流回归,梯度增强树木,随机森林和支持矢量机器模型的信号检测性能.
- 用美国食品和药物管理局不良事件报告系统 (FAERS) 的数据来评估这些模型.
主要方法:
- 一个使用FAERS数据从2017年第四季度到2020年第四季度的横截面研究设计.
- 实施两套培训套件:一个是对结果变量分层的,另一个是使用合成少数人过量采样技术 (SMOTE) 来解决数据不平衡.
- 为每个算法开发原始和超参数调整模型.
- 使用准确度,精度,F1得分,回忆,ROCAUC和PRCAUC等指标进行性能评估.
主要成果:
- 在平衡数据集上训练的模型表现出比在SMOTE增强数据上训练的模型更高的准确性,F1得分和回忆力.
- 后勤回归,梯度增强树,随机森林和支持矢量机器模型在平衡数据集上训练时表现出类似的性能指标.
- 超参数调整的梯度增强树木模型实现了最高的ROCAUC (0.646),而原始随机森林模型在平衡数据集上产生了最高的PRCAUC (0.839).
结论:
- 所有评估的机器学习模型在平衡训练集上表现相似.
- 后勤回归模型显示出更高的准确性,精度和回忆.
- 将不成比例分析结果与额外的案例报告信息相结合,与单独不成比例分析相比,显著改善了模型性能指标.
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