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A Bayesian latent class approach for EHR-based phenotyping.

Rebecca A Hubbard1, Jing Huang1, Joanna Harton1

  • 1Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, Pennsylvania.

Statistics in Medicine
|September 26, 2018
PubMed
Summary
This summary is machine-generated.

A new Bayesian latent phenotyping approach improves the accuracy of identifying patient characteristics from electronic health records, especially for type 2 diabetes mellitus (T2DM) research.

Keywords:
Bayesianelectronic health recordslatent classmissing dataphenotype

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Area of Science:

  • Biomedical Informatics
  • Clinical Research Methods
  • Data Science in Healthcare

Background:

  • Electronic health records (EHRs) are crucial for research, but phenotyping faces challenges due to imperfect data.
  • Current EHR phenotyping often relies on rule-based methods with limited accuracy.
  • Missing data in EHRs, particularly laboratory results, poses significant hurdles.

Purpose of the Study:

  • To introduce a Bayesian latent phenotyping approach for EHR data.
  • To address imperfect data elements and missing not at random (MNAR) patterns.
  • To provide a method for phenotyping when gold-standard data is unavailable.

Main Methods:

  • Developed a Bayesian latent phenotyping model.
  • Conducted simulation studies comparing latent class and rule-based phenotyping.
  • Applied the latent phenotype approach to a large cohort of children at risk for type 2 diabetes mellitus (T2DM) using the PEDSnet data.

Main Results:

  • In simulations, the latent class approach showed similar sensitivity (95.9%) but higher specificity (99.7%) than rule-based methods.
  • Biomarkers and clinical codes were strongly associated with latent T2DM status in the PEDSnet cohort.
  • Missingness in glucose and hemoglobin A1c was predicted by latent T2DM status, indicating MNAR patterns.

Conclusions:

  • The Bayesian latent phenotyping approach offers a robust alternative to rule-based methods.
  • This method effectively handles imperfect data and MNAR missingness in EHR research.
  • Latent phenotyping can significantly enhance the accuracy of identifying patient cohorts for studies like T2DM research.