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Updated: Apr 26, 2026
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
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Clinical document metadata extraction: A scoping review
Kurt Miller1, Qiuhao Lu2, William Hersh3
1Bioinformatics and Computational Biology Program, University of Minnesota, Rochester, MN, USA; Center for Digital Health, Mayo Clinic, Rochester, MN, USA.
Journal of Biomedical Informatics
|April 24, 2026
Summary
Automated clinical document metadata extraction research is advancing rapidly, driven by large language models. This progress enables more sophisticated clinical text processing and integration into healthcare workflows.
Area of Science:
- Medical Informatics
- Natural Language Processing
- Health Information Management
Background:
- Clinical document metadata (e.g., document type, author role) is crucial for interpreting health information.
- Documentation heterogeneity and temporal drift pose challenges to metadata harmonization.
- Automated extraction methods are vital for coalescing metadata from diverse sources into standardized schemas.
Purpose of the Study:
- To systematically review research on clinical document metadata extraction.
- To identify trends in methodologies and applications of metadata extraction.
- To pinpoint research gaps in the field of clinical document metadata.
Main Methods:
- A scoping review following PRISMA-ScR guidelines was conducted.
- Searches were performed across multiple databases (Ovid MEDLINE, EMBASE, Scopus, Web of Science) and external sources.
- 77 relevant articles published between 2011 and 2025 were included in the full-text review.
Main Results:
- The review included 77 articles: 49 methodological, 22 application-focused, and 6 metadata composition analyses.
- Methods have evolved from rule-based/traditional machine learning to transformer-based architectures.
- Publicly available labeled data for metadata extraction remains limited, except for structural section datasets.
Conclusions:
- Research in clinical document metadata extraction has seen significant acceleration.
- Large language models are enhancing generalizability and enabling advanced clinical text processing.
- Future research is expected to focus on richer metadata representations and deeper integration into clinical applications.
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