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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Semantic retrieval and navigation in clinical document collections.

Markus Kreuzthaler1, Philipp Daumke2, Stefan Schulz1

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This study enhances electronic health records by semantically enriching medical texts. This improves navigation and searchability of patient documentation for better clinical insights.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Information Management

Background:

  • Patients with chronic diseases generate extensive documentation across multiple treatments.
  • Managing and navigating this large volume of medical text in electronic health records (EHRs) is challenging.

Purpose of the Study:

  • To develop and demonstrate a system for semantic enrichment of medical texts.
  • To improve recall-oriented navigation and searchability within a patient's complete documentation.
  • To explore the adaptability of the technology for personalized health records and secondary data use.

Main Methods:

  • Utilized a de-identified dataset of 1,696 discharge summaries for prototyping.
  • Employed a natural language processing (NLP) toolset for text annotation (UIMA) and indexing (Solr).
  • Developed a browser-based platform integrating document import, search, and navigation with concept-based querying and dynamic facets.

Main Results:

  • Demonstrated the feasibility of semantic document enrichment for individual patient records.
  • The integrated search engine effectively combines free text and concept-based queries.
  • Dynamically generated facets include diagnoses, procedures, medications, lab values, and body parts, enhancing search.

Conclusions:

  • Semantic enrichment of medical texts is feasible and beneficial for patient documentation navigation.
  • The developed technology can enhance clinical workplaces, personalized health records, and facilitate cohort building for research.