图形表示基于学习的固定长度临床特征向量生成从异质医疗记录
Tomohisa Seki1, Yoshimasa Kawazoe1,2, Kazuhiko Ohe1,3
1Department of Healthcare Information Management, The University of Tokyo Hospital, Japan.
Studies in health technology and informatics
|January 25, 2024
概括
本研究引入了一种机器学习方法,用于将电子健康记录数据转换为数值向量. 无监督图形嵌入方法有效提取临床信息,提高患者再入院的预测准确度.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 电子医疗数据的手动特征设计是耗时的,需要专业知识.
- 将患者数据转换为数值向量对于医疗保健中的机器学习应用至关重要.
研究的目的:
- 开发一种基于机器学习的自动化方法,用于从电子医疗数据中提取特征.
- 评估无监督图形嵌入用于表示临床信息的有效性.
主要方法:
- 使用Graph2Vec算法在异质图上利用无监督学习.
- 采用机器学习模型,根据提取的图形嵌入,预测30天的患者再入院.
主要成果:
- 拟议的图形嵌入方法显著改善了患者再入院的预测性能.
- 在图形表示中增加了信息,从而提高了预测准确性.
结论:
- 无监督图形嵌入方法是临床信息学特征设计的合适和高效方法.
- 该技术自动从电子病历中提取临床相关信息.
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