PubMed · 1997 · 242 citations · 3 references
ComputingBiomedical InformaticsHealth InformaticsClinical InformaticsData ManagementData MiningClinical AnalyticsClinical Knowledge RepresentationData PreprocessingData WarehousingKnowledge DiscoveryText MiningClinical Data MiningClinical Data WarehousesClinical Knowledge DiscoveryData Query WorkflowData Warehousing ArchitectureHidden KnowledgeKnowledge Discovery ProcessesMedical Data Warehousing
Clinical databases contain vast patient information, yet few methods exist to uncover hidden relationships that could yield new medical knowledge. The study applies data mining techniques to a large clinical database to discover relationships and to describe the mining processes. The authors evaluated data from 3,902 obstetrical patients using exploratory factor analysis to identify factors related to preterm birth. Three factors were identified for further exploration.
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.
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