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Medical data mining: knowledge discovery in a clinical data warehouse.

机译:医学数据挖掘:临床数据仓库中的知识发现。

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摘要

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.
机译:临床数据库已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,很少有方法被开发出来并用于发现这种隐藏的知识。在这项研究中,数据挖掘技术(也称为数据库中的知识发现)用于搜索大型临床数据库中的关系。具体来说,使用探索性因素分析法评估了3,902名产科患者的数据,以寻找可能有助于早产的因素。研究者确定了三个因素以进行进一步的探索。本文描述了挖掘临床数据库所涉及的过程,包括数据仓库,数据查询和清理以及数据分析。

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