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Jorge R Herskovic1, Devika Subramanian, Trevor Cohen

  • 1School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA. jorge.r.herskovic@uth.tmc.edu

BMC Bioinformatics
|January 17, 2013
PubMed
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This study introduces a new graph-based method for high-throughput phenotyping using electronic health records. This approach significantly improves the accuracy of identifying breast cancer patients compared to existing tools.

Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Health Services Research

Background:

  • Electronic Health Records (EHRs) in Clinical Data Warehouses (CDWs) offer potential for Comparative Effectiveness Research.
  • The utility of CDWs is limited by the scarcity of accurately labeled data.
  • High-throughput phenotyping is crucial for leveraging EHR data.

Purpose of the Study:

  • To develop and evaluate a novel unsupervised, graph-based approach for high-throughput phenotyping.
  • To integrate knowledge from CDWs, biomedical literature, and the Unified Medical Language System (UMLS).
  • To phenotype breast cancer patients using the developed approach and compare its performance against MetaMap.

Main Methods:

  • Automatic construction of a graphical knowledge model.

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  • Integration of data from Clinical Data Warehouses, biomedical literature, and UMLS.
  • Application of the model for unsupervised high-throughput phenotyping of breast cancer patients.

    • Main Results:

    • MetaMap achieved 51.1% accuracy (Recall=85.4%, Precision=26.2%, F1=40.1%) for breast cancer patient identification.
    • The proposed unsupervised graph-based phenotyping achieved 84.1% accuracy (Recall=46.3%, Precision=61.2%, F1=52.8%).

    Conclusions:

    • The novel graph-based approach demonstrates superior performance for unsupervised high-throughput phenotyping.
    • This method presents a promising alternative for efficiently and accurately labeling patient data in CDWs.
    • The findings support the integration of diverse knowledge sources for enhanced clinical data analysis.