OLR-Net:对象标签检索网络用于主要诊断提取
Kai Wang1, Xin Tan2, Shan Nan3
1Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou 570228, China; School of Information and Communication Engineering, Hainan University, Haikou 570228, China.
Computers in biology and medicine
|September 17, 2024
概括
从医疗记录中提取主要诊断是具有挑战性的,因为数据有限. OLR-Net模型有效地使用标签本地化和检索来识别主要诊断,即使只有很少的培训示例.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 从患者出院摘要中自动提取主要诊断对于医疗数据利用至关重要.
- 手动提取是耗时的;由于训练数据有限,现有的自动模型在罕见的诊断中扎.
研究的目的:
- 开发一种方法,从排放摘要中提取主要诊断,特别针对有限数据的场景.
主要方法:
- 提出了包含语义提取,标签本地化和标签检索的对象标签检索网络 (OLR-Net).
- 利用一维卷积神经网络进行诊断本地化,以提高罕见诊断的性能.
- 使用命中率,平均相互排名和接收器操作特征曲线 (AUROC) 下的面积来评估性能.
主要成果:
- 在5个数据设置中对12788份瘤学出院摘要进行了测试 (完整,前50名,几次射击,一次射击,零次射击).
- 在使用完整数据集时,获得了0.8778的高HR@5和0.9851的宏观AUROC.
- 在有限的数据设置中表现出强的表现,宏观AUROC为0.9833的几次拍摄数据和0.9485的一次拍摄数据.
结论:
- OLR-Net 模型显示,从排放摘要中提取主要诊断的巨大潜力,特别是当数据有限时.
- 标签本地化和检索是允许在低数据场景中有效提取的关键组件.
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