title: Medical data mining: knowledge discovery in a clinical data warehouse.
abstract: 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.
authors.affiliation: Division of Medical Informatics, Duke University Medical Center, Durham, North Carolina, USA.
authors.forename: J C
authors.initials: JC
authors.lastname: Prather
authors.forename: D F
authors.initials: DF
authors.lastname: Lobach
authors.forename: L K
authors.initials: LK
authors.lastname: Goodwin
authors.forename: J W
authors.initials: JW
authors.lastname: Hales
authors.forename: M L
authors.initials: ML
authors.lastname: Hage
authors.forename: W E
authors.initials: WE
authors.lastname: Hammond
mesh_terms: D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005163:Factor Analysis, Statistical; D005260:Female; D006801:Humans; D016347:Medical Records Systems, Computerized; D018743:Perinatal Care; D011247:Pregnancy / Q000706:statistics & numerical data*; D011256:Pregnancy Outcome