Identifying and mitigating biases in EHR laboratory tests.

ABSTRACT: Electronic health record (EHR) data show promise for deriving new ways of modeling human disease states. Although EHR researchers often use numerical values of laboratory tests as features in disease models, a great deal of information is contained in the context within which a laboratory test is taken. For example, the same numerical value of a creatinine test has different interpretation for a chronic kidney disease patient and a patient with acute kidney injury. We study whether EHR research studies are subject to biased results and interpretations if laboratory measurements taken in different contexts are not explicitly separated. We show that the context of a laboratory test measurement can often be captured by the way the test is measured through time. We perform three tasks to study

SUBMITTER: Pivovarov R 

PROVIDER: S-EPMC4194228 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature