Development and validation of phenotype classifiers across multiple sites in the observational health data sciences and informatics network.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 32374408

Abstract

OBJECTIVE: Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network.

Authors

  • Mehr Kashyap
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.
  • Martin Seneviratne
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • Juan M Banda
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
  • Thomas Falconer
    Department of Biomedical Informatics, Columbia University, New York, New York, USA.
  • Borim Ryu
    Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • Sooyoung Yoo
    Office of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
  • George Hripcsak
    Department of Biomedical Informatics, Columbia University, 622 W 168th Street, PH20, New York, NY 10032, USA; Medical Informatics Services, NewYork-Presbyterian Hospital, 622 W 168th Street, PH20, New York, NY 10032, USA. Electronic address: hripcsak@columbia.edu.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

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