Your browser doesn't support javascript.
loading
Adapting Scoring Based Classification to Simplify and Automate Phenotype Creation for Cohort Identification in Clinical Data.
Wang, Pei; Pullen, Daniel L; Garza, Maryam Y; Zozus, Meredith N.
Afiliação
  • Wang P; University of Arkansas for Medical Sciences College of Medicine, Department of Biomedical Informatics, Little Rock, Arkansas, USA.
  • Pullen DL; University of Arkansas at Little Rock, Department of Information Science, Little Rock, Arkansas, USA.
  • Garza MY; University of Arkansas for Medical Sciences College of Medicine, Department of Biomedical Informatics, Little Rock, Arkansas, USA.
  • Zozus MN; University of Arkansas for Medical Sciences College of Medicine, Department of Biomedical Informatics, Little Rock, Arkansas, USA.
AMIA Jt Summits Transl Sci Proc ; 2019: 488-494, 2019.
Article em En | MEDLINE | ID: mdl-31259003
EHR-based phenotype development and validation are extremely time-consuming and have considerable monetary cost. The creation of a phenotype currently requires clinical experts and experts in the data to be queried. The new approach presented here demonstrates a computational alternative to the classification of patient cohorts based on automatic weighting of ICD codes. This approach was applied to data from six different clinics within the University of Arkansas for Medical Science (UAMS) health system. The results were compared with phenotype algorithms designed by clinicians and informaticians for asthma and melanoma. Relative to traditional phenotype development, this method shows potential to considerably reduce time requirements and monetary costs with comparable results.

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2019 Tipo de documento: Article