Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.

@article{Newton2013ValidationOE,
  title={Validation of electronic medical record-based phenotyping algorithms: results and lessons learned from the eMERGE network.},
  author={Katherine M. Newton and Peggy L. Peissig and Abel N. Kho and Suzette J. Bielinski and Richard L. Berg and Vidhu Choudhary and Melissa Basford and Christopher G. Chute and Iftikhar J. Kullo and Rongling Li and Jennifer A. Pacheco and Luke V. Rasmussen and Leslie Spangler and Joshua C. Denny},
  journal={Journal of the American Medical Informatics Association : JAMIA},
  year={2013},
  volume={20 e1},
  pages={e147-54}
}
BACKGROUND Genetic studies require precise phenotype definitions, but electronic medical record (EMR) phenotype data are recorded inconsistently and in a variety of formats. OBJECTIVE To present lessons learned about validation of EMR-based phenotypes from the Electronic Medical Records and Genomics (eMERGE) studies. MATERIALS AND METHODS The eMERGE network created and validated 13 EMR-derived phenotype algorithms. Network sites are Group Health, Marshfield Clinic, Mayo Clinic, Northwestern… CONTINUE READING
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