医学数据挖掘：临床数据仓库中的知识发现
Medical data mining: knowledge discovery in a clinical data warehouse.
作者信息
Prather J C, Lobach D F, Goodwin L K, Hales J W, Hage M L, Hammond W E
机构信息
Division of Medical Informatics, Duke University Medical Center, Durham, North Carolina, USA.
出版信息
Proc AMIA Annu Fall Symp. 1997:101-5.
Clinical databases have accumulated large quantities of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Unfortunately, few methodologies have been developed and applied to discover this hidden knowledge. In this study, the techniques of data mining (also known as Knowledge Discovery in Databases) were used to search for relationships in a large clinical database. Specifically, data accumulated on 3,902 obstetrical patients were evaluated for factors potentially contributing to preterm birth using exploratory factor analysis. Three factors were identified by the investigators for further exploration. This paper describes the processes involved in mining a clinical database including data warehousing, data query and cleaning, and data analysis.
临床数据库积累了大量有关患者及其医疗状况的信息。这些数据中的关系和模式可以提供新的医学知识。不幸的是，很少有方法被开发和应用来发现这些隐藏的知识。在本研究中，数据挖掘技术（也称为数据库中的知识发现）被用于在一个大型临床数据库中寻找关系。具体而言，使用探索性因素分析对3902名产科患者积累的数据进行评估，以寻找可能导致早产的因素。研究人员确定了三个因素以供进一步探索。本文描述了挖掘临床数据库所涉及的过程，包括数据仓库、数据查询与清理以及数据分析。
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