糖尿病血糖预测的异质时间表示
Yaohui Huang1,2, Zhikai Ni3, Zhenkun Lu1,2
1College of Electronic Information, Guangxi Minzu University, Nanning, China.
Frontiers in physiology
|August 3, 2023
概括
这项研究引入了一种新的基于图形的网络,称为HETER,用于多患者血糖预测. HETER有效地处理各种患者数据,提高糖尿病监测的预测准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 血糖预测 (BGP) 有助于糖尿病患者监测和临床决策.
- 多患者BGP是由于异质和不确定的持续血糖监测 (CGM) 数据而具有挑战性.
研究的目的:
- 提出第一个基于图形的异质时间表示 (HETER) 网络,用于多患者BGP.
- 解决CGM数据中的数据异质性和不确定性,以改善BGP.
主要方法:
- 赫特利用灵活的次序重复 (SSR) 方法进行数据对齐.
- 它以图形形式构建并学习样本之间的关系,以捕捉全球时间特征.
- 包含一个临时增强的机制和局部依赖的线性残留融合.
主要成果:
- 在两个医院的112名患者的现实数据上验证了HETER.
- 与第二好的方法相比,实现了最大的改善31.42% (MAE),27.18% (MAPE) 和34.85% (RMSE).
- 在多个患者的BGP场景中证明了稳定性和准确性.
结论:
- HETER有效地整合了全球和本地时间信息,以减轻多患者CGM数据中的异质性和不确定性.
- 该方法显示了扩展到涉及结构化医疗数据的其他临床任务的潜力.
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