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• W3083461656 abstract "Abstract The ‘discovery’ stage of genome-wide association studies required amassing large, homogeneous cohorts. In order to attain clinically useful insights, we must now consider the presentation of disease within our clinics and, by extension, within our medical records. Large-scale use of electronic health record (EHR) data can help to understand phenotypes in a scalable manner, incorporating lifelong and whole-phenome context. However, extending analyses to incorporate EHR and biobank-based analyses will require careful consideration of phenotype definition. Judgements and clinical decisions that occur ‘outside’ the system inevitably contain some degree of bias and become encoded in EHR data. Any algorithmic approach to phenotypic characterization that assumes non-biased variables will generate compounded biased conclusions. Here, we discuss and illustrate potential biases inherent within EHR analyses, how these may be compounded across time and suggest frameworks for large-scale phenotypic analysis to minimize and uncover encoded bias." @default.
• W3083461656 created "2020-09-11" @default.
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• W3083461656 date "2020-09-02" @default.
• W3083461656 modified "2023-10-14" @default.
• W3083461656 title "Implicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data" @default.
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• W3083461656 doi "https://doi.org/10.1093/hmg/ddaa192" @default.
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