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A Semi-Automatic Framework to Identify Abnormal States in EHR Narratives.

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|January 4, 2018
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This study introduces a semi-automatic framework to map electronic health records (EHR) to disease ontologies by extracting information from clinical narratives. The method effectively maps 18%-33% of abnormal disease states, aiding medical information systems.

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Area of Science:

  • Medical Informatics
  • Clinical Data Analysis
  • Ontology Engineering

Background:

  • Disease ontologies are crucial knowledge bases for medical information systems.
  • Automatic mapping between electronic health records (EHR) and disease ontologies is essential for clinical application.
  • A significant portion (41%) of abnormal states in chronic disease ontologies require information extraction from clinical narratives.

Purpose of the Study:

  • To present a semi-automatic framework for identifying abnormal states in clinical narratives.
  • To facilitate the creation of mapping modules between EHR and disease ontologies.
  • To evaluate the effectiveness of the proposed framework in data mapping.

Main Methods:

  • Analysis of ontologies for 148 chronic diseases.
  • Development of a semi-automatic framework for information extraction from clinical narratives.
  • Evaluation of the framework's performance in mapping abnormal states to disease ontologies.

Main Results:

  • The proposed semi-automatic framework is effective for data mapping in 18%-33% of abnormal states within chronic disease ontologies.
  • Identified specific abnormal states where the method is less effective for information extraction from clinical narratives.
  • Provides insights into the challenges of mapping clinical narratives to structured disease ontologies.

Conclusions:

  • The semi-automatic framework offers a valuable approach for bridging EHR data and disease ontologies.
  • Further research is needed to address the limitations in extracting information for certain abnormal states.
  • The study contributes to improving the utility of disease ontologies in real-world clinical settings.