Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.

Journal: Annual review of biomedical data science
Published Date: May 23, 2018

Abstract

With the widespread adoption of electronic health records (EHRs), large repositories of structured and unstructured patient data are becoming available to conduct observational studies. Finding patients with specific conditions or outcomes, known as phenotyping, is one of the most fundamental research problems encountered when using these new EHR data. Phenotyping forms the basis of translational research, comparative effectiveness studies, clinical decision support, and population health analyses using routinely collected EHR data. We review the evolution of electronic phenotyping, from the early rule-based methods to the cutting edge of supervised and unsupervised machine learning models. We aim to cover the most influential papers in commensurate detail, with a focus on both methodology and implementation. Finally, future research directions are explored.

Authors

  • Juan M Banda
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.
  • Martin Seneviratne
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • Nigam H Shah
    Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA.

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