Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.
Hum Mol Genet
; 29(R1): R33-R41, 2020 09 30.
Article
en En
| MEDLINE
| ID: mdl-32879975
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Prejuicio
/
Enfermedad
/
Biología Computacional
/
Polimorfismo de Nucleótido Simple
/
Estudio de Asociación del Genoma Completo
/
Registros Electrónicos de Salud
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Hum Mol Genet
Asunto de la revista:
BIOLOGIA MOLECULAR
/
GENETICA MEDICA
Año:
2020
Tipo del documento:
Article
País de afiliación:
Estados Unidos