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Updated: Jun 5, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

A System for Automated Extraction of Metadata from Scanned Documents using Layout Recognition and String Pattern

Dharitri Misra, Siyuan Chen, George R Thoma

    Archiving : Final Program and Proceedings. IS & T'S Archiving Conference
    |October 1, 2011
    PubMed
    Summary
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    Automated metadata extraction (AME) reduces archiving costs by using machine learning to find and extract information from digital documents. This system improves access to historical records, like those from the Food and Drug Administration.

    Area of Science:

    • Digital archiving
    • Information retrieval
    • Machine learning

    Background:

    • Manual metadata acquisition is costly and time-consuming for digital document archiving.
    • Metadata embedded within documents is crucial for discovery and access.
    • Automated methods are needed to efficiently extract this metadata.

    Purpose of the Study:

    • To develop and describe an automated metadata extraction (AME) system.
    • To reduce the cost and improve the efficiency of archiving digital documents.
    • To present a novel approach for extracting embedded metadata from textual documents.

    Main Methods:

    • Developed an AME system combining layout classification/recognition models with a metadata pattern search model.
    • Utilized Support Vector Machine and Hidden Markov Models for layout recognition.

    Related Experiment Videos

    Last Updated: Jun 5, 2026

    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
    07:50

    A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

    Published on: September 20, 2018

  • Employed a rule-based metadata search model analyzing string patterns for extraction.

    • Main Results:

    • Successfully extracted metadata from a historic Food and Drug Administration collection.
    • Demonstrated the system's effectiveness for structured and semi-structured text corpora.
    • The AME system offers a cost-effective solution for metadata acquisition.

    Conclusions:

    • The AME system provides an efficient and automated approach to metadata extraction.
    • The system shows promise for adapting to similar historical document collections.
    • Ongoing enhancements aim to further improve the AME system's capabilities.