Normalization of Phenotypic Data from a Clinical Data Warehouse: Case Study of Heterogeneous Blood Type Data with Surprising Results.
Clinical data warehouses often contain analogous data from disparate sources, resulting in heterogeneous formats and semantics. We have developed an approach that attempts to represent such phenotypic data in its most atomic form to facilitate aggregation. We illustrate this approach with human blood antigen typing (ABO-Rh) data drawn from the National Institutes of Health's Biomedical Translational Research Information System (BTRIS). In applying the method to actual patient data, we discovered a 2% incidence of changed blood types. We believe our approach can be applied to any institution's data to obtain comparable patient phenotypes. The actual discrepant blood type data will form the basis for a future study of the reasons for blood typing variation.
Laboratory for Informatics Development, the Clinical Center of the US National Institutes of Health, Bethesda, Maryland, USA.
James J
JJ
Cimino
D000017:ABO Blood-Group System; D001788:Blood Grouping and Crossmatching / Q000592:standards* / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods; D066264:Datasets as Topic / Q000145:classification / Q000592:standards / Q000706:statistics & numerical data*; D057286:Electronic Health Records / Q000145:classification / Q000592:standards / Q000706:statistics & numerical data*; D063005:Health Information Systems / Q000145:classification / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods / Q000592:standards; D009316:National Institutes of Health (U.S.); D009323:Natural Language Processing; D010641:Phenotype; D012016:Reference Values; D014481:United States; D018875:Vocabulary, Controlled
D000017:ABO Blood-Group System