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A knowledge-based, automated method for phenotyping in the EHR using only clinical pathology reports.

Alexandre Yahi1, Nicholas P Tatonetti1

  • 1Department of Biomedical Informatics, Department of Systems Biology, Department of Medicine, Columbia University, New York, NY, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|August 26, 2015
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Summary
This summary is machine-generated.

This study introduces ORPheUS, an automated method for electronic health record (EHR) phenotyping using lab reports. It effectively identifies disease cohorts for biomedical discovery.

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Area of Science:

  • Biomedical Informatics
  • Health Informatics
  • Computational Biology

Background:

  • Secondary use of electronic health records (EHR) offers significant potential for biomedical discovery.
  • EHR-phenotyping, or cohort identification, is crucial but challenging, especially for complex phenotypes requiring manual definitions.
  • Existing methods can be limited by clinical biases and manual effort.

Purpose of the Study:

  • To present Ontology-driven Reports-based Phenotyping from Unique Signatures (ORPheUS), an automated EHR-phenotyping approach.
  • To demonstrate the efficacy of using unique signatures from clinical pathology reports for cohort identification.
  • To mitigate clinical biases by focusing solely on lab report data.

Main Methods:

  • ORPheUS identifies unique signatures within abnormal clinical pathology reports.
  • These signatures are mapped to pre-defined medical terms from biomedical ontologies.
  • The system was used to generate disease signatures for 858 conditions.

Main Results:

  • ORPheUS successfully generated signatures for 858 diseases.
  • Validation against reference cohorts for Type 2 Diabetes Mellitus (T2DM) and Atrial Fibrillation (AF) showed effectiveness.
  • The approach using only clinical pathology reports proved effective for automated phenotyping.

Conclusions:

  • Automated phenotyping using clinical pathology reports is a viable strategy for EHR research.
  • ORPheUS offers an efficient method for primary screening in automated clinical phenotyping.
  • This approach enables exploration of EHR data dimensions beyond traditional clinical biases.