HEART:学习使用异质关系感知变压器更好地表示EHR数据
Tinglin Huang1, Syed Asad Rizvi1, Rohan Krishna Thakur1
1Department of Computer Science, Yale University, United States.
Journal of biomedical informatics
|October 30, 2024
概括
一个新的模型HEART有效地使用电子健康记录 (EHR) 中医疗实体之间的异质相关性,以改进患者结果预测. 这种方法增强了EHR代表性学习,并提供了可解释的见解.
科学领域:
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
背景情况:
- 电子健康记录 (EHR) 包含有价值的患者数据,但现有的语言模型难以捕捉不同医疗实体之间的复杂关系.
- 当前的方法往往对医疗实体 (诊断,药物,程序,实验室测试) 均或只关注诊断,限制性能.
- 需要先进的EHR表示学习,明确模拟不同类型的医疗实体之间的异质相关性.
研究的目的:
- 开发一个基础的语言模型,预先训练了EHR数据,明确纳入医疗实体之间的异质相关性.
- 通过超越同质编码和单个实体的重点来改善EHR表示学习.
主要方法:
- 建议HEART (EHR的异质关系意识变压器),这是一个针对EHR数据设计的新型变压器模型.
- 整合异质实体,并使用关系嵌入来进行复杂的推理来表示双对关系.
- 采用多层次的注意力计划,以有效地连接遭遇,并利用两个预训任务:缺失实体预测和异常检测.
主要成果:
- 在两个EHR数据集和五个下游任务上,HEART在四个最先进的基础模型上展示了卓越的性能.
- 与Med-BERT相比,在死亡 (12.1%) 和再入院 (4.1%) 预测方面取得了显著的改善.
- 案例研究证实了HEART通过学习嵌入来提供对实体关系的可解释见解的能力.
结论:
- HEART有效地利用了EHR中医疗实体之间的异质关系,以改善代表性学习.
- 该模型的多级编码和专门的预训练目标提高了效率和有效性.
- 实验结果验证了HEART在医疗保健应用中的强大性能和实际实用性.
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