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Next generation phenotyping using the unified medical language system.

Tomasz Adamusiak1, Naoki Shimoyama, Mary Shimoyama

  • 1Human and Molecular Genetics Center, Medical College of Wisconsin, Milwaukee, WI, United States. tomasz@mcw.edu.

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|January 21, 2015
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Summary

This study developed a terminology-agnostic method to integrate diverse electronic health record (EHR) data, enabling comprehensive clinical phenotyping. The Unified Medical Language System (UMLS) effectively interfaces with multiple health care data standards.

Keywords:
CPTHCPCSICD-10ICD-9LOINCRxNormSNOMED CTUMLSmeaningful usesemantic interoperability

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Area of Science:

  • Health Informatics
  • Clinical Data Management
  • Biomedical Data Integration

Background:

  • Electronic Health Records (EHRs) increase accessibility of patient data for research.
  • Navigating numerous disparate clinical terminology standards presents a significant challenge.
  • Standardized data formats are crucial for leveraging EHR information.

Purpose of the Study:

  • To develop a methodology for integrating large volumes of structured clinical information.
  • To create a system that is terminology-agnostic and captures heterogeneous clinical phenotypes.
  • To define phenotyping as the extraction of all clinically relevant features from EHRs.

Main Methods:

  • Utilized Common Meaningful Use (MU) Dataset terminology standards (SNOMED CT, RxNorm, LOINC, CPT, HCPCS, ICD-9-CM, ICD-10-CM).
  • Employed the Unified Medical Language System (UMLS) as a mapping layer among ontologies.
  • Implemented an extract, load, transform (ELT) approach and integrated terminologies into a single, optimized UMLS-derived ontology.

Main Results:

  • Evaluated methodology with simulated data from the Clinical Avatars project (100,000 virtual patients).
  • Deployed the methodology at scale using EHR data from 7931 patients (12 million clinical observations) at Froedtert Hospital.
  • A demonstration using Clinical Avatars data is available online.

Conclusions:

  • The Unified Medical Language System (UMLS) can effectively interface with diverse clinical data standards.
  • The developed methodology facilitates the integration of structured clinical information despite complexity.
  • This approach supports comprehensive clinical phenotyping from EHR data.