在电子健康记录中缺失值的基于注意的归算表格数据表格数据
Ibna Kowsar1, Shourav B Rabbani1, Manar D Samad1
1Department of Computer Science, Tennessee State University, Nashville, TN, United States.
Proceedings. IEEE International Conference on Healthcare Informatics
|October 10, 2024
概括
这项研究引入了一个基于注意力的框架,用于在电子健康记录中赋值缺失的值,显著提高机器学习模型的准确性. 这种新的方法通过有效地重建不完整的表格数据来增强患者特定的预测建模.
科学领域:
- 机器学习 机器学习
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 在电子健康记录 (EHR) 表格数据中输入缺失值 (IMV) 对于准确的患者特定预测建模至关重要.
- 现有的深度学习方法在表格数据方面遇到了困难,这限制了他们在IMV中的成功.
- 当前的统计和机器学习归算方法在处理复杂数据模式方面存在局限性.
研究的目的:
- 提出一种新的基于注意力的归算框架,用于重建表式EHR数据中缺失的值.
- 通过使用对比学习技术来增强归算模型的概括性.
- 根据最先进的归算技术,评估拟议方法的性能.
主要方法:
- 开发了一种基于注意力的新型缺失值归算框架,利用自我注意或样本间注意.
- 采用了从对比学习到改进模型概括的数据操纵策略.
- 利用五个不同的表格数据集和两个EHR数据集进行全面评估.
主要成果:
- 拟议的自我注意归算方法显著优于现有的基于统计和决策树的归算方法.
- 实现了正常化根平均平方误差 (RMSE) 的实质性降低:一般表格数据上18.4%74.7%,EHR数据上52.6%82.6%.
- 在完全随机的缺失假设下,在各种缺失水平 (10%50%) 中表现出卓越的表现.
结论:
- 基于注意力的归算框架为处理表式EHR数据中缺失的值提供了一种优越的方法.
- 该方法显示了通过更准确的数据重建来改善患者特定预测建模的巨大潜力.
- 这项工作推进了IMV用于表格数据的领域,特别是在医疗保健应用中.
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