Generating and evaluating a propensity model using textual features from electronic medical records.

ABSTRACT: BACKGROUND:Propensity score (PS) methods are commonly used to control for confounding in comparative effectiveness studies. Electronic health records (EHRs) contain much unstructured data that could be used as proxies for potential confounding factors. The goal of this study was to assess whether the unstructured information can also be used to construct PS models that would allow to properly deal with confounding. We used an example of coxibs (Cox-2 inhibitors) vs. traditional NSAIDs and the risk of upper gastro-intestinal bleeding as example, since this association is often confounded due to channeling of coxibs to patients at higher risk of upper gastro-intestinal bleeding. METHODS:In a cohort study of new users of nonsteroidal anti-inflammatory drugs (NSAIDs) from the Dutch Integrated

SUBMITTER: Afzal Z 

PROVIDER: S-EPMC6398864 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature