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Evaluating resources composing the PheMAP knowledge base to enhance high-throughput phenotyping.

Nicholas C Wan1, Ali A Yaqoob2, Henry H Ong2

  • 1Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA.

Journal of the American Medical Informatics Association : JAMIA
|December 1, 2022
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Summary
This summary is machine-generated.

An ensemble strategy for high-throughput phenotyping (HTP) using electronic health records (EHRs) significantly improves accuracy and recall. This approach leverages dissimilar online resources to enhance phenotyping data capture from EHRs.

Keywords:
electronic health recordshigh-throughput phenotypingnatural language processingonline health information

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Area of Science:

  • Biomedical Informatics
  • Health Informatics
  • Computational Biology

Background:

  • High-throughput phenotyping (HTP) using electronic health records (EHRs) relies on online resources for disease descriptions.
  • Variations in the quality and content of these online resources can impact phenotyping accuracy.
  • The previous PheMAP study combined independent resources for HTP, but individual resource performance varied.

Purpose of the Study:

  • To evaluate the phenotyping performance of single resource-based PheMAPs.
  • To investigate an optimized strategy for HTP by addressing variations in online resource quality.
  • To develop and assess an ensemble method for improved HTP.

Main Methods:

  • Compared top-ranked concept unique identifiers (CUIs) from individual resources against the original PheMAP using Jaccard similarity.
  • Correlated resource-based CUIs with established Phenotype KnowledgeBase (PheKB) algorithms for specific diseases (hypothyroidism, T2DM, dementias).
  • Implemented an ensemble strategy classifying patient status based on resource agreement for improved phenotyping.

Main Results:

  • Jaccard similarity matrices showed variability in CUIs across single resource-based PheMAPs.
  • MedlinePlus and MedicineNet-derived PheMAPs showed superior performance but incomplete phenotype coverage (81.6%).
  • The proposed PheMAP-Ensemble demonstrated higher average accuracy and precision than single resources and improved recall over the original PheMAP.

Conclusions:

  • Individual online resources yield varying phenotyping performance within the PheMAP framework.
  • An ensemble method significantly enhances PheMAP's quality by integrating diverse resources.
  • This optimized strategy improves accurate phenotyping data capture from EHRs.