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一种新型的间接方法,通过循环数据清理,通过多测试模式的自我组织地图 (SOM-clean) 指导,来导出参考间隔

Kiyoshi Ichihara¹, Teppei Yamashita², Anwar Borai³

¹Faculty of Health Sciences, Department of Clinical Laboratory Sciences, Yamaguchi University Graduate School of Medicine, Minami-Kogushi 1-1-1, Ube, 755-0001, Japan.

Computer methods and programs in biomedicine

|February 20, 2026

概括

一个新的软件,SOM-clean,使用多变量数据挖掘从实验室数据中导出准确的参考间隔 (RI). 这种方法改进了传统的单变量方法,用于更健康的患者数据解释.

科学领域:

临床化学临床化学
生物信息学是一种生物信息学.
数据挖掘数据挖掘

背景情况:

目前用于推导参考区间 (RIs) 的现有方法通常使用单变量方法,数据清理有限.
多变量数据挖掘策略为更严格的RI导出提供了潜力.

研究的目的:

开发和评估新型软件SOM-clean,用于从常规实验室数据库中间接推导参考间隔 (RI).
在实验室数据中使用自组织地图 (SOM) 聚类来代排除非典型的多测试模式.

主要方法:

从沙特阿拉伯实验室数据库 (37,285 条记录) 中检索了 22 项主要的健康查测试 (HST).
应用了参数的Box-Cox转换和对初始RI值的值的标准化.
使用自组织地图 (SOM) 聚类来代识别和排除非典型记录.
在稳定之前反复计算RI,优化SOM参数以获得最佳的适合性.

主要成果:

在几乎所有HST中,SOM-clean实现了RI的优异适用性 (GOF),表明符合健康状态.
与refineR方法相比,SOM-clean RI的广度和偏差较小,特别是在偏差分布方面.
通过引用直接研究RI来优化GOF,以确定最佳的SOM地图大小和细胞排除标准.

结论:

SOM-clean是间接RI估计的实用和强大的参数工具.
该软件使用了一种新的多变量数据清理方案,以提高准确性.
这种方法提高了从常规实验室数据中获得的RI的可靠性.

关键词:

数据挖掘是一种数据挖掘.实验室信息系统实验室信息系统动力转换动力转换动力转换参考时间间隔是指参考时间间隔.两个参数的Box-Cox公式统一的尺度统一的尺度

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