Suppr超能文献

[血液酶数据的因果模型与组织状况可视化]

[A causal model of blood enzyme data and visualization of tissue conditions].

作者信息

Inada M, Kinpara K, Igarashi F

机构信息

Department of Clinical Chemistry, Toranomon Hospital, Tokyo.

出版信息

Rinsho Byori. 1999 Jan;47(1):61-9.

Abstract

Quantitative diagnostics is an important field in which clinical data are converted into medical information. A variety of approaches to obtain medical diagnoses have been developed and multivariate statistical analysis supports the diagnostic process. Although many clinical data are affected by body conditions such as disease and functional failure, only a few models take this phenomenon into consideration. The correlation between laboratory test results can be understood as a causal relationship between body conditions and clinical test data variations. A multivariate statistical method, factor analysis, expresses a causal relationship between latent variables and observed variables. We developed a causal model for blood enzyme data using factor analysis. The latent variables were assumed to be organ specific regarding 9 enzyme data. This causal model expressed clinical knowledge within blood enzymes and allowed visualization of organ conditions. The visualization of laboratory data is useful to screen patient's pathological states.

摘要

定量诊断是一个重要领域,临床数据在其中被转化为医学信息。已经开发出多种获取医学诊断的方法,多变量统计分析为诊断过程提供支持。尽管许多临床数据会受到疾病和功能衰竭等身体状况的影响,但只有少数模型考虑到了这一现象。实验室检测结果之间的相关性可以理解为身体状况与临床检测数据变化之间的因果关系。一种多变量统计方法——因子分析,表达了潜在变量与观测变量之间的因果关系。我们使用因子分析为血液酶数据建立了一个因果模型。对于9种酶数据,潜在变量被假定为器官特异性的。这个因果模型表达了血液酶中的临床知识,并能直观显示器官状况。实验室数据的直观显示有助于筛查患者的病理状态。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验